SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Questions and Answers

Key Requirements:

Protect public endpoints (CloudFront distributions and ALBs) frommalicious attacks.

Centralizedmanagementacross multiple accounts in an organization.

Ability tomonitor security configurationseffectively.

Minimizeoperational overhead.

Analysis of Options

Option A:

AWS WAF:Protects web applications by filtering and blocking malicious requests. Rules can be applied to both ALBs and CloudFront distributions.

AWS Firewall Manager:Enables centralized management of WAF rules across multiple accounts in an AWS Organizations organization. It simplifies rule deployment, avoiding the need to configure rules individually in each account.

AWS Config:Monitors compliance by using rules that check Regional and global WAF configurations. Ensures that security configurations align with organizational policies.

Operational Overhead:Centralized management and automated monitoring reduce the operational burden.

Correct Approach:Meets all requirements with the least overhead.

Option B:

This approach involves applying WAF rules in each account manually.

While AWS Config and AWS Security Hub provide monitoring capabilities, managing individual WAF configurations in multiple accounts introduces significant operational overhead.

Incorrect Approach:Higher overhead compared to centralized management with AWS Firewall Manager.

Option C:

Similar to Option A but includesAmazon Inspector, which is not designed for monitoring WAF configurations.

AWS Security Hubis appropriate for monitoring but is redundant when Firewall Manager and Config are already in use.

Incorrect Approach:Adds unnecessary complexity and does not focus on monitoring WAF specifically.

Option D:

AWS Shield Advanced:Focuses on mitigating large-scale DDoS attacks but does not provide the fine-grained web application protection offered by WAF.

AWS Config:Can monitor Shield Advanced configurations but does not fulfill the WAF monitoring requirements.

Incorrect Approach:Does not address the need for WAF or centralized rule management.

Why Option A is Correct

Protection:

AWS WAF provides fine-grained filtering and protection against SQL injection, cross-site scripting, and other web vulnerabilities.

Rules can be applied at both ALBs and CloudFront distributions, covering all public endpoints.

Centralized Management:

AWS Firewall Manager enables security teams to centrally define and manage WAF rules across all accounts in the organization.

Monitoring:

AWS Config ensures compliance with WAF configurations by checking rules and generating alerts for misconfigurations.

Operational Overhead:

Centralized management via Firewall Manager and automated compliance monitoring via AWS Config greatly reduce manual effort.

AWS Solution Architect References

AWS WAF Documentation

AWS Firewall Manager Documentation

AWS Config Best Practices

AWS Organizations Documentation

AWS Budgetsis the native service for creating budget thresholds and sending notifications whenactualorforecastedcosts exceed those thresholds. AWS documentation states that Budgets can send alerts toAmazon SNSand supports both actual and forecasted alerts without requiring custom code. Cost Explorer is still useful for analyzing and monitoring cost trends, but Budgets is the correct alerting tool. The other options introduce unnecessary Lambda logic or misuse other services. Since the question specifically says the company does not want to build a custom solution, a budget with SNS alerts is the most direct and least operationally heavy answer.

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The correct answer isCbecause storing large binary objects such as documents directly in a relational database increases database size, raises storage cost, and can degrade performance over time.Amazon S3is a more appropriate service for storing large unstructured objects like documents, images, and media files. The application can store the actual document files in S3 and keep only the related metadata, object keys, and references in the existing Oracle database.

This design is more cost-effective because S3 storage is generally less expensive than expanding high-performance relational database storage for BLOB content. It also improves database performance because the database no longer needs to manage the overhead of storing and retrieving large document payloads. The database can focus on transactional and relational data, which is the workload it is designed to handle best.

S3 also provides high durability, scalability, and resilience. These characteristics satisfy the requirement for a highly available and resilient solution while avoiding unnecessary expansion of the RDS storage layer. Keeping metadata in the existing database minimizes the application changes compared with a full database migration.

Option A is incorrect because Magnetic storage would significantly reduce performance and is not appropriate for this workload. Option B would improve performance, but it would be much more expensive and does not address the root cause of storing large BLOBs in a relational database. Option D is incorrect because moving the relational Oracle workload to DynamoDB would require a major redesign and is not the most cost-effective solution for the stated problem.

The best architectural pattern is to useAmazon S3 for document storageand the relational database for metadata and transactional records.

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

Amazon FSx for Lustre is the ideal solution for high-performance computing (HPC) workloads that require parallel access to a shared file system with low latency. FSx for Lustre is designed specifically to meet the needs of such workloads, offering sub-millisecond latencies, which makes it well-suited for the 1 ms latency requirement mentioned in the question.

Here is why FSx for Lustre is the best fit:

Parallel File System: FSx for Lustre is a parallel file system that can scale across hundreds of Amazon EC2 instances, providing high throughput and low-latency access to data. It is optimized for processing large datasets in parallel, which is essential for HPC workloads.

Low Latency: FSx for Lustre is capable of providing access latencies well within 1 ms, making it ideal for performance-sensitive workloads like HPC.

Seamless Integration with Amazon S3: FSx for Lustre can be linked to an Amazon S3 bucket. This integration allows data to be imported from S3 into FSx for Lustre before the workload begins and exported back to S3 after processing. This feature is crucial for manual postprocessing because it enables engineers to access the dataset in S3 after processing.

Performance: FSx for Lustre is built for workloads that require high performance, such as machine learning, analytics, media processing, and financial simulations, which are typical for HPC environments.

In contrast:

Amazon EFS (Option A): While EFS provides shared file storage and scales across multiple EC2 instances, it does not offer the same level of performance or sub-millisecond latencies as FSx for Lustre. EFS is more suited for general-purpose workloads, not high-performance computing.

Mounting S3 as a file system (Option B and D): S3 is object storage, not a file system designed for low-latency access and parallel processing. Mounting S3 buckets directly or using AWS Resource Access Manager to share the bucket would not meet the low-latency (1 ms) or performance requirements needed for HPC workloads.

Therefore, Amazon FSx for Lustre (Option C) is the most appropriate and verified solution for this scenario.

AWS References:

Amazon FSx for Lustre

Best Practices for High Performance Computing (HPC)

Amazon FSx and Amazon S3 Integration

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

For spiky retail order traffic, a serverless architecture is a strong fit because it scales automatically without pre-provisioning servers. API Gateway can receive the order requests, Lambda can process them on demand, and DynamoDB on-demand mode can absorb unpredictable bursts without capacity planning. This design is more elastic than the EC2-based answers and avoids the single-instance bottlenecks in A and B. Option D is scalable, but it involves more infrastructure management and reader nodes do not directly solve write-heavy order-ingestion spikes. Therefore, API Gateway plus Lambda plus DynamoDB on-demand is the most resilient and scalable solution for this high-traffic event-driven workload.

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Correct Approach:

AWS Security Groups:

Security groups operate at the instance level, making them the ideal tool for controlling access to specific resources such as an Amazon RDS database.

By default, security groups deny all incoming traffic. You can allow access by explicitly specifying another security group.

Associating an RDS database security group with the EC2 instances ' security group ensures only the specified EC2 instances can access the RDS database.

Incorrect Options Analysis:

Option A: Using CIDR blocks for IP-based access is less secure and more difficult to manage. Additionally, Auto Scaling groups dynamically allocate IP addresses, making this approach impractical.

Option B: Network ACLs (NACLs) operate at the subnet level and are stateless. While NACLs can deny or allow traffic, they are not suited to application-specific access control.

Option D: Similar to Option B, using a NACL with CIDR ranges for EC2 IPs is difficult to manage and not application-specific.

Amazon S3 is the natural long-term storage service for terabytes of images and videos because it scales massively and integrates directly withAmazon SageMaker AIfor machine learning workflows. UsingS3 Intelligent-Tieringhelps optimize storage cost automatically when access patterns vary over time. AWS recommends Intelligent-Tiering when access frequency changes or is unpredictable, because the service automatically moves objects between tiers while keeping them available. The alternatives introduce extra file system copies and additional cost or use a notebook instance as a storage endpoint, which is not a scalable long-term ingestion design. Therefore, S3 with direct SageMaker integration and Intelligent-Tiering is the most scalable and cost-conscious solution. (AWS Documentation)

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Amazon RDS Multi-AZ deployments provide automatic failover for relational databases such as MySQL, ensuring high availability and durability. The feature maintains synchronous replication between a primary DB instance and a standby in a separate Availability Zone. AWS guarantees that failover typically completes within minutes, ensuring an RTO and RPO of less than 2 minutes. Option A requires manual promotion of replicas, which cannot meet the strict RTO/RPO requirement. Option C depends on custom scripts and manual synchronization, introducing operational risk. Option D creates active-active EC2-based databases, which do not provide synchronous replication or automated failover. Therefore, Multi-AZ RDS (B) is the managed, resilient, and operationally efficient solution that meets the business requirements.

Amazon S3 Intelligent-Tiering is the most cost-effective solution for this scenario, providing both high availability and durability while adjusting automatically to changing access patterns. It moves data across two access tiers: one optimized for frequent access and another for infrequent access, based on usage patterns. This tiering ensures that the company avoids paying for unused storage while also keeping frequently accessed data in a more accessible tier.

Key AWS references and benefits ofS3 Intelligent-Tiering:

High Durability and Availability: Amazon S3 offers 99.999999999% durability and 99.9% availability for objects stored, ensuring data is always protected.

Automatic Tiering: Data is automatically moved between tiers based on access patterns, making it ideal for workloads with unpredictable or variable access patterns.

No Retrieval Fees: Unlike S3 One Zone-IA or Glacier, there are no retrieval fees, making this more cost-effective in scenarios where access patterns vary over time.

AWS Documentation: According to the AWS Well-Architected Framework under theCost Optimization Pillar, S3 Intelligent-Tiering is recommended for storage when access patterns change over time, as it minimizes costs while maintaining availability​.

Amazon Route 53 Multivalue Answer Routing: This routing policy allows Route 53 to return multiple values, such as IP addresses, in response to DNS queries. This can distribute traffic across multiple resources and includes health checks to ensure traffic is only routed to healthy instances.

Health Checks:

Configure health checks for each Region to monitor the health of the website instances.

Route 53 will only include healthy instances in the DNS responses, ensuring that traffic is not routed to an unhealthy Region.

Load Distribution and Disaster Recovery:

Multivalue answer routing helps balance the load between instances in different Regions.

If instances in one Region become unhealthy, Route 53 will route traffic to the healthy instances in the other Region.

Operational Simplicity: This solution does not require complex configurations or additional resources, making it a simple yet effective way to distribute traffic and ensure high availability.

Amazon EC2 On-Demand Instances: Since the batch jobs cannot be disrupted, On-Demand Instances provide the necessary reliability and availability.

Amazon S3 Standard: Storing data in S3 Standard for the first 30 days ensures quick and frequent access.

S3 Glacier Deep Archive: After 30 days, moving data to S3 Glacier Deep Archive significantly reduces storage costs for data that is rarely accessed.

S3 Lifecycle Configuration: Automating the transition and deletion of objects using lifecycle policies ensures cost optimization and compliance with data retention requirements.

The right solution isAPI Gateway + Lambda + Amazon Comprehend + DynamoDB. AWS documents that Amazon Comprehend is the NLP service for detecting entities, sentiment, and key phrases, which matches the workload exactly. DynamoDB is a fully managed, distributed database designed for high-throughput workloads and single-digit millisecond performance at scale, making it a strong fit for a highly available application handling many concurrent requests. The other answers misuse services: Amazon Translate is for translation, Amazon Textract is for extracting text from documents, and ElastiCache is a cache rather than the durable primary database described in the question. This makes option A the only architecture that aligns with both the NLP functions and the scalable database requirement. (AWS Documentation)

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Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.

Explanation (AWS Docs):

ElastiCache provides an in-memory cache layer for frequently accessed queries, reducing latency and offloading read pressure from the database.

“You can improve database performance by caching frequently accessed data using Amazon ElastiCache.”

— ElastiCache Overview

Business-critical applications often require predictable, low-latency I/O and high durability. Provisioned IOPS SSD (io1 or io2) Amazon EBS volumes are specifically engineered for these workloads.

Option C provides consistent, high-performance storage with guaranteed IOPS and low latency. EBS volumes are network-attached and persist independently of the EC2 instance lifecycle, ensuring durability and data protection. Provisioned IOPS volumes are commonly used for databases, transactional systems, and latency-sensitive applications.

Option A (instance store) offers low latency but is ephemeral and loses data on instance stop or failure. Option B is in-memory caching and not durable storage. Option D is optimized for large, sequential workloads and does not provide consistent low latency.

Therefore, C is the correct choice because it delivers the required performance, durability, and reliability for mission-critical applications.

Amazon Macie: Detects sensitive data such as PII in S3 buckets using machine learning.

EventBridge Rule: Filters Macie findings for specific sensitive data events (e.g., SensitiveData).

SNS Notification: Provides real-time alerts to the security team for immediate action.

Amazon Macie Documentation,Amazon EventBridge Documentation

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

" S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements. "

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

The correct answer isBbecause the application isMicrosoft Windows-based, requiresshared storage, must use theSMB protocol, and needs to beresilient.Amazon FSx for Windows File Serveris the AWS managed file storage service built specifically for Windows workloads. It provides nativeSMB access, integration with Windows environments, and managed shared file storage for multiple application servers.

AMulti-AZ deploymentis the key feature that satisfies the resilience requirement. Multi-AZ FSx for Windows File Server stores data synchronously across Availability Zones and can automatically fail over to a standby file server in another Availability Zone if the primary becomes unavailable. This ensures high availability for shared file access and reduces the risk of application disruption.

Option A is incorrect becauseAmazon S3is object storage and does not natively provide SMB shared file semantics in the way required by Windows applications. Option C is incorrect becauseAmazon EBSvolumes are block storage volumes that are generally attached to a single instance and are not the standard solution for resilient multi-instance shared SMB storage. Option D is incorrect becauseAmazon FSx File Gatewayis used to provide on-premises access to Amazon FSx file systems and is not the primary shared storage service required for this cloud-native application design.

AWS storage guidance recommendsAmazon FSx for Windows File Serverfor Windows applications that need managed file shares over SMB. When resilience is required, aMulti-AZ deploymentis the most appropriate architecture.

AWS provides EBS Block Public Access at the organization level in AWS Organizations. When enabled, it prevents any EBS snapshot—across all member accounts—from being shared publicly.

This is an organization-wide control implemented centrally, with no need for per-account configuration, monitoring rules, or custom IAM policy enforcement.

AWS Config (Option A) would only detect issues after they occur. IAM restrictions (Option C) are less effective because snapshot permission changes can occur through multiple paths. CloudTrail (Option D) only logs events and does not block public sharing.

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Usingcross-account IAM rolesis the most secure and scalable way to share data between AWS accounts.

Atrust relationshipallows the analytics team ' s account to assume the role in the main account and access the DynamoDB table directly.

Ais feasible but involves data duplication and additional costs for storing the JSON files in S3.

B and Cviolate security best practices by allowing public access to sensitive data and sharing credentials, which is highly discouraged.

AWS Documentation Reference:

Cross-Account Access with Roles

Best Practices for Amazon DynamoDB Security

Amazon EC2 allows you to install and manage SQL Server directly on a Windows or Linux VM, giving you full access to the underlying operating system. This is required when you need to install custom agents, configure OS-level features, or have complete control over the environment.

Amazon RDS and Aurora are managed services and do not provide access to the underlying OS, nor does Redshift (which is a data warehouse, not SQL Server).

AWS Documentation Extract:

" If you require access to the underlying operating system or need to install custom software, use SQL Server on Amazon EC2. Amazon RDS is a managed service and does not provide OS-level access. "

(Source: AWS Database Migration documentation, SQL Server Deployment Options)

The best multi-Region DR design isAurora global databasefor the database layer andS3 Cross-Region Replicationfor object storage. AWS documentation states that Aurora global database automatically synchronizes changes from a primary cluster to secondary clusters in other AWS Regions, providing multi-Region resiliency and fast recovery from Region-level outages. AWS also documents thatS3 CRRcopies objects to a destination bucket in another AWS Region, which is the standard way to protect S3 data across Regions. Same-Region options do not satisfy the multi-Region requirement, and the backup-based alternatives are less aligned with minimizing potential data loss than continuous cross-Region replication. Therefore,Aurora global database plus S3 CRRis the correct answer. (AWS Documentation)

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Amazon EMR instance fleets are the correct design because they provide the most flexible and operationally efficient way to launch clusters across different EC2 instance types, Availability Zones, and purchasing options. In this scenario, the company wants On-Demand Instances for core nodes, Spot Instances for task nodes, and memory optimized instances only where capacity is available. Instance fleets allow EMR to evaluate multiple subnets in multiple Availability Zones and provision capacity where the requested instance types and purchase models are available. Instance groups are more rigid because they are tied to a more fixed instance configuration. A single-subnet design also fails the Availability Zone flexibility requirement. Therefore, using EMR instance fleets across multiple subnets is the best answer.

S3 Object Lock in Compliance Mode prevents objects from being deleted or overwritten for a fixed, specified retention period. Compliance Mode is specifically designed to meet regulatory requirements, ensuring that no user, including the root user, can modify or delete the object during the retention period.

Reference Extract:

" S3 Object Lock in compliance mode protects objects from being deleted or overwritten during the specified retention period, helping meet regulatory retention requirements. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Object Lock and Compliance section.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

AWS Database Migration Service can migrate relational data toAmazon S3 in Apache Parquet format, which is a strong archival format for analytics. AWS also documents that Parquet in S3 can be queried byAmazon Athena, which removes the need to keep a database engine running just for annual reports. This is much lower-overhead than maintaining RDS or EC2 instances solely for occasional querying. Among the options, D is the only one that uses a serverless query service over archived relational data. Glacier Flexible Retrieval also fits archived data better than keeping a live relational instance. This makes D the most cost-effective and least operationally heavy approach. (docs.aws.amazon.com)

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To route S3 traffic privately from within a VPC, AWS provides Gateway VPC Endpoints for Amazon S3. These allow private connectivity to S3 without traversing the public internet or requiring an Internet Gateway.

From AWS Documentation:

“A gateway endpoint enables you to privately connect your VPC to supported AWS services such as Amazon S3 and DynamoDB without requiring an Internet Gateway, NAT device, or public IP.”

(Source: Amazon VPC User Guide – Gateway Endpoints)

Why A is correct:

Gateway VPC endpoints route S3 traffic internally within the AWS network.

Improves security and data privacy while reducing exposure to the public internet.

Requires only a simple route table modification and IAM policy configuration.

Why other options are incorrect:

B: S3 is a regional service; you cannot “move” it inside a VPC.

C: Access points do not change the routing path; still uses S3 endpoints.

D: AWS Direct Connect is for hybrid environments, not intra-AWS private connectivity.

AWS Lambda is not suitable for long-running jobs that can take up to 20 minutes, as Lambda has a maximum execution duration of 15 minutes.

Amazon ECS with AWS Fargate allows you to run containers without managing EC2 instances, providing a scalable and highly available environment. You can scale Fargate tasks to handle large and parallel video processing jobs. Amazon Aurora is a highly available, managed relational database. S3 Intelligent-Tiering is cost-effective for storing large video files with variable access patterns.

AWS Documentation Extract:

" AWS Fargate lets you run containers without managing servers or clusters, providing a highly available and scalable compute environment. Fargate is suitable for running data processing workloads that require long-running compute tasks. "

(Source: AWS Fargate documentation, Use Cases)

A: Lambda is not suitable for long-running (over 15 min) or heavy compute jobs.

C: Amazon EMR is optimized for big data analytics, not specific video processing. FSx for Lustre and Kinesis are not best fit for this use case.

D: EKS with EC2 adds operational overhead and RDS in a single AZ is not highly available; Glacier Deep Archive is not suitable for frequently accessed video files.

For logs that are:

Written and processed within a short period (24 hours)

Accessed quickly for compute/analytics

With unknown object count and size

Amazon S3 Standard is the most appropriate and cost-effective. Intelligent-Tiering is designed for data stored for longer periods (typically 30+ days) with changing access patterns and charges a per-object monitoring and automation fee that becomes inefficient for very short-lived objects.

S3 Glacier Deep Archive and S3 One Zone-IA are optimized for long-term archival or infrequently accessed data with retrieval time or availability constraints that are not suitable for nightly active processing.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

This solution is the most scalable and efficient way to handle large volumes of survey data while automating sentiment analysis:

API Gateway and SQS: The survey results are sent to API Gateway, which forwards the data to an SQS queue. SQS can handle large volumes of messages and ensures that messages are not lost.

AWS Lambda: Lambda is triggered by polling the SQS queue, where it processes the survey data.

Amazon Comprehend: Comprehend is used for sentiment analysis, providing insights into customer satisfaction.

DynamoDB with TTL: Results are stored in DynamoDB with aTime to Live (TTL)attribute set to expire after 365 days, automatically removing old data and reducing storage costs.

Option B (EC2 API): Running an API on EC2 requires more maintenance and scalability management compared to API Gateway.

Option C (S3 and Rekognition): Amazon Rekognition is for image and video analysis, not sentiment analysis.

Option D (Amazon Lex): Amazon Lex is used for building conversational interfaces, not sentiment analysis.

AWS References:

Amazon Comprehend for Sentiment Analysis

Amazon SQS

DynamoDB TTL

The best answer ismessage batching. AWS documentation explains that SQS batch actions let applications send, receive, or delete up to 10 messages at a time. AWS also notes that batching can be combined with horizontal scaling to improve throughput with fewer requests and connections. That makes batching the most relevant feature for handling sudden traffic increases efficiently. FIFO queues address strict ordering and deduplication, visibility timeout controls how long a received message stays hidden from other consumers, and long polling reduces empty responses and unnecessary polling cost. None of those features directly improve throughput as effectively as batching for traffic spikes.

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To capture DNS query and response data, including response codes, Amazon Route 53 provides query logging, which is the most precise and AWS-supported solution for this requirement.

Option A enables Route 53 query logging, which records detailed information about DNS queries, such as the queried domain, record type, source IP, and DNS response code. These logs are delivered to Amazon CloudWatch Logs, where administrators can search, analyze, and retain them for forensic investigation and root cause analysis.

Option B is incorrect because AWS CloudTrail records API calls to AWS services, not DNS query traffic. Option C provides aggregated metrics (such as query counts and health checks) but does not include per-query response codes. Option D offers best-practice recommendations but does not collect or analyze DNS query data.

Therefore, A is the correct solution because Route 53 query logging provides the detailed, low-level DNS visibility required for troubleshooting and operational analysis.

Why Option B is Correct:

AWS WAF: Provides a simple way to create geographic match rules to block or allow traffic based on country IP ranges.

Least Operational Overhead: Attaching the WAF rule to the ALB ensures centralized control without modifying ACLs or instance firewalls.

Why Other Options Are Not Ideal:

Option A: Network ACLs operate at the subnet level and can become complex to manage for dynamic or evolving IP ranges.

Option C: Managing IP-based rules in security groups and ACLs lacks scalability and does not provide country-based filtering.

Option D: Configuring host-based firewalls increases operational overhead and does not leverage AWS-managed solutions.

AWS References:

AWS WAF Geomatch:AWS Documentation - WAF Geomatch

A Stored Volume Gateway stores the entire dataset locally on-premises while asynchronously and securely backing up the data to AWS. This meets the requirement for full local access and automatic transfer to AWS.

Cached Volume Gateway (Option C) keeps only frequently accessed data locally; the full dataset is stored in AWS, not on-premises.

Snowball and Snowball Edge (Options A and B) are migration tools, not ongoing backup solutions.

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Option Ais the most automated and cost-efficient solution for exporting data to S3 for analytics.

Option Binvolves manual setup of Streams to S3.

Options C and Dintroduce complexity with EMR.

AWS Fargate runs containers without managing servers, and ECS Service Auto Scaling adjusts the number of tasks based on demand. Behind an Application Load Balancer, ECS services elastically scale out to handle concurrent long-running requests. This fits workloads with processing times of 5–20 minutes per request. Lambda (C, D) has a maximum 15-minute timeout, so a portion of requests would fail or require complex refactoring and orchestration. Option B relies on vertical scaling and manual intervention, leaving capacity idle and failing to scale automatically with spikes. By containerizing the app and running on ECS with Fargate, the company gains automatic scaling, isolation per task, and no instance management, ensuring reliable throughput as request volume grows while keeping operations minimal.

For application access to AWS services from ECS tasks, the correct identity is the ECS task role. AWS separates the task execution role, which is mainly for pulling images and sending logs, from the task role, which is used by the application code running inside the container. Because the application itself needs to read from S3, the least-effort and most secure design is to grant the necessary S3 permissions directly to the task role. Using IAM users and access keys would introduce unnecessary credential management and reduce security. Therefore, assigning the required S3 permissions to the task role is the proper AWS best-practice solution.

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The correct answers areA and D. The requirement is to replace third-party middleware with an AWS-native solution that supportsasynchronous communication,exactly-once delivery, and theleast infrastructure management.Amazon SQS FIFO queuesare the best messaging component because FIFO queues are designed for workloads that requireordered processingandexactly-once message deliverysemantics. That makes optionDthe correct messaging choice.

For the compute layer,AWS Lambdaprovides the least infrastructure management because it is a fully managed serverless compute service. Lambda removes the need to manage virtual machines, operating systems, or cluster infrastructure. It integrates naturally with queue-based asynchronous architectures and can be invoked to process messages from SQS while scaling automatically based on queue depth and workload demand. That makes optionAthe best choice for minimizing operational burden.

Option B is incorrect because EC2 instances would require the company to manage instance provisioning, patching, scaling, and availability. Option C is incorrect becauseAmazon SNSis a pub/sub messaging service and does not provide exactly-once delivery guarantees likeSQS FIFO. Option E is incorrect becauseAmazon EKSintroduces Kubernetes management overhead and is not the lowest-management compute option.

AWS best practices recommend using managed messaging and serverless compute to reduce undifferentiated operational work. For a migration that requires asynchronous processing with exactly-once delivery and minimal infrastructure management, the most appropriate architecture isAWS Lambda functionswithAmazon SQS FIFO queues.

You cannot encrypt an existing unencrypted RDS DB instance in place. AWS guidance for encrypting an existing RDS PostgreSQL instance is totake a snapshot, create an encrypted copy of that snapshot, and restore a new DB instance from the encrypted snapshot. AWS also documents that encryption uses AWS KMS keys and that customer managed keys are the appropriate choice when you want explicit control over the key used for the new encrypted instance. That makesBthe required migration step andEthe correct key-management step. Option C is invalid because encryption cannot simply be turned on for the existing instance, and option D is not how you create a new AWS-managed default key.

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AWS Direct Connect: Provides a dedicated network connection from your on-premises data center to AWS, ensuring low latency and consistent network performance.

Direct Connect Gateway Association:

Direct Connect Gateway: Acts as a global network transit hub to connect VPCs across different AWS regions.

Association with Transit Gateway: Enables communication between on-premises data centers and multiple VPCs connected to the transit gateway.

Transit Virtual Interface (VIF):

Create Transit VIF: To connect Direct Connect with a transit gateway.

Setup Steps:

Establish a Direct Connect connection.

Create a transit VIF to the Direct Connect gateway.

Associate the Direct Connect gateway with the transit gateway attached to the VPCs.

Cost Efficiency: This combination avoids the recurring costs and potential performance variability of VPN connections, providing a robust, low-latency hybrid network solution.

The requirement to “have access to the operating system” means the compute layer must be Amazon EC2 (or containers on EC2). Managed runtimes such as Lambda and Fargate do not provide OS-level access.

The requirement for a “low-latency NoSQL database” maps directly to Amazon DynamoDB, which is a fully managed NoSQL key-value and document database that provides single-digit millisecond latency at any scale.

Because the application runs in a private subnet, AWS best practice is to access DynamoDB privately via a VPC endpoint (gateway endpoint for DynamoDB). This avoids traversing the public internet and simplifies security.

An instance profile (EC2 role) is the recommended method to grant EC2 instances permission to access DynamoDB without hardcoding credentials.

Why the other options are not correct:

B: Lambda does not provide OS access, which violates the security requirement.

C: Fargate does not provide OS access, and Aurora PostgreSQL is a relational database, not NoSQL.

D: S3 + Athena is an analytics pattern, not a low-latency NoSQL database solution; query latency is much higher and not suitable for OLTP-style app storage.

This question centers on improving scalability and global access performance.

Auto Scaling groups enable EC2 instances to scale dynamically in response to demand, ensuring availability during peak hours without manual intervention. Amazon RDS read replicas offload read traffic, improving read throughput and reducing latency on the primary database instance. Deploying Amazon CloudFront with S3 as origin accelerates delivery of static transaction documents globally by caching content at edge locations, reducing latency for users worldwide.

Option B focuses on vertical scaling (larger instances) and caching with ElastiCache, but it does not address global content delivery optimally. AWS Global Accelerator accelerates network traffic but is better suited for accelerating TCP and UDP traffic; CloudFront is generally preferred for HTTP content delivery.

Option C migrates workloads to Lambda and Aurora global databases, which is an advanced and potentially costly redesign that may not be necessary. Option D suggests moving to ECS and multi-AZ RDS but does not address global content delivery efficiently.

Therefore, option A uses proven scalability and caching best practices aligned with AWS Well-Architected Framework pillars for performance and operational excellence.

AWS recommends usingSystems Manager Session Managerinstead of direct SSH for administrative access because it provides browser-based or CLI-based access without bastion hosts and supports centralized audit logging. To allow private instances to reach Systems Manager without internet exposure, the VPC needs the appropriateinterface VPC endpoint. The managed instances also need the proper instance permissions, and AWS documentation points to theAmazonSSMManagedInstanceCorepolicy for that purpose. A gateway endpoint is not used for Session Manager, and attaching a user-facing Session Manager policy to the EC2 instances themselves is the wrong permission model. This makesB and Ethe most operationally efficient and secure combination. (AWS Documentation)

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AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.

EC2 Auto Scaling warm pools allow you to pre-initialize instances, reducing the delay in scale-out events. This results in significantly faster response times during demand surges while remaining cost-effective compared to always running at peak capacity.

Key Requirements:

Serverless architecture.

Handle traffic bursts with high availability.

Process orders asynchronouslyin the order they are received.

Analysis of Options:

Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.

Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once. AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently. This satisfies all requirements.

Option C:Amazon SQS standard queues do not guarantee message order and have " at-least-once " delivery, making them unsuitable for the FIFO requirement.

Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.

AWS References:

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration

Latency-based routing in Amazon Route 53 is designed to route users to the Region that provides the lowest network latency, based on Amazon’s measurements of latency between AWS Regions and users’ networks. For applications deployed in multiple Regions, this provides the highest performance experience for global users.

Therefore, creating an A record with a latency routing policy is the correct choice.

Geolocation (Option B) routes based on user location, which may not always correspond to the lowest latency.

Failover (Option C) is for active-passive architectures, not performance optimization.

Geoproximity (Option D) is more complex and focused on directing traffic based on geographic bias rather than measured latency.

AWS Backup is a fully managed backup service designed to centralize and automate data protection across AWS services including EC2 and RDS. It allows users to define backup schedules (backup plans) and automatically create and retain backups. AWS Backup also offers restore testing plans, allowing users to automate the validation of backups by restoring them in a controlled manner. This service is built to minimize operational overhead by removing the need to manage custom scripts, manual processes, or additional orchestration services. This aligns with AWS best practices for resilience, automation, and operational excellence.

Reference Extract from AWS Documentation / Study Guide:

" AWS Backup enables you to centralize and automate data protection across AWS services. You can create backup plans, schedule backups, and set lifecycle policies. AWS Backup also enables restore testing to verify your backup integrity, with minimal manual intervention. "

Source: AWS Certified Solutions Architect – Official Study Guide, Resiliency and Disaster Recovery section; AWS Backup User Guide.

To securely integrate Amazon S3 with an application that uses Amazon Cognito for user authentication, the following two steps are essential:

Step 1: Create an Amazon Cognito Identity Pool (Option A)

Amazon Cognito Identity Poolsallow users to obtain temporary AWS credentials to access AWS resources, such as Amazon S3, after successfully authenticating with the Cognito user pool. The identity pool bridges the gap between user authentication and AWS service access by generating temporary credentials using AWS Identity and Access Management (IAM).

Once a user logs in using theCognito User Pool, the identity pool providesIAM roles with specific permissionsthat the application can use to access S3 securely. This ensures that each user has appropriate access controls while accessing the S3 bucket.

This is a secure way to ensure that users only have temporary and least-privilege access to the S3 bucket for their documents.

Step 2: Create an Amazon S3 VPC Endpoint (Option C)

By creating anAmazon S3 VPC endpoint, the company ensures that communication between the application (which is hosted in a private subnet) and the S3 bucket occurs over theAWS private network, without the need to traverse the internet. This enhances security and prevents exposure of data to public networks.

TheVPC endpointallows the application to access the S3 bucket privately and securely within the VPC. It also ensures that traffic stays within the AWS network, reducing attack surface and improving overall security.

Why the Other Options Are Incorrect:

Option B: This is incorrect becauseAmazon Cognito User Poolsare used for user authentication, not for generating S3 access tokens. To provide S3 access, you need to useAmazon Cognito Identity Pools, which offer AWS credentials.

Option D: ANAT gatewayis unnecessary in this scenario. Using aVPC endpointfor S3 access provides a more secure and cost-effective solution by keeping traffic within AWS.

Option E: Attaching a policy to restrict access based on IP addresses is not scalable or efficient. It would require managing users’ dynamic IP addresses, which is not an effective security measure for this use case.

AWS References:

Amazon Cognito Identity Pools

Amazon VPC Endpoints for S3

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

Setting the RDS backup retention policy to 30 days forautomated backupsthrough AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are notautomatically managed by RDS ' s retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

AWS Global Accelerator provides static anycast IP addresses that remain constant regardless of Regional failover. AWS directs traffic to the optimal healthy Region without relying on DNS TTL values, eliminating the risk of stale DNS caches.

Route 53 routing (Options B and D) still depends on DNS caching behavior. CloudFront (Option A) is for content delivery, not Regional failover for applications.

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AWS Step Functions can orchestrate Lambda executions without modifying the Lambda code, and the Map state is designed to run a Lambda function once per item in a list—perfect for activating many devices in parallel or controlled batches.

This eliminates the loop inside the Retrieve function and prevents timeouts.

EventBridge (Option A) only changes the schedule. DynamoDB Streams (Option B) is unrelated to the activation workflow. EC2 (Option D) increases operational overhead.

AWS Budgets allows you to set custom cost and usage budgets. When actual or forecasted usage exceeds the threshold, AWS Budgets can automatically send alerts to an Amazon SNS topic. You can use AWS Cost Explorer in parallel for visual tracking of spending. This solution requires no code and has minimal operational overhead.

A. API Gateway + DynamoDB:Provides high scalability, low latency, and seamless integration with Amazon Comprehend for NLP tasks.

B. HTTP API + Translate + ElastiCache:Translate is not relevant for NLP tasks like sentiment analysis or entity recognition. ElastiCache is unsuitable for permanent storage.

C. SQS + EC2 + RDS:Increases complexity and operational overhead. RDS may not scale effectively for high concurrent loads.

D. WebSocket API + Textract:Textract is irrelevant for NLP tasks. WebSocket API is not the optimal choice for this use case.

The most cost-effective and scalable solution for generating thumbnails when photos are uploaded to an S3 bucket is to useS3 event notificationsto trigger anAWS Lambda function. This approach avoids the need for a long-running EC2 instance or EMR cluster, making it highly cost-effective because Lambda only charges for the time it takes to process each event.

S3 Event Notifications: Automatically triggers the Lambda function when a new photo is uploaded to the S3 bucket.

AWS Lambda: A serverless compute service that scales automatically and only charges for execution time, which makes it the most economical choice when dealing with periodic events like photo uploads.

The Lambda function can generate the thumbnail and upload it to a second S3 bucket, fulfilling the requirement efficiently.

Option AandOption B(EMR or EC2 with scheduled scripts)**: These are less cost-effective as they involve continuously running infrastructure, which incurs unnecessary costs.

Option D (S3 Storage Lens): S3 Storage Lens is a tool for storage analytics and is not designed for event-based photo processing.

AWS References:

Amazon S3 Event Notifications

AWS Lambda Pricing

The best answer isAPI Gateway + SQS + DLQ + Lambdabecause it combines API ingestion with durable queuing and asynchronous processing. AWS guidance for event-driven serverless architectures specifically recommends usingAmazon SQSto decouple components and durably persist messages when downstream processing is slower or bursty. AWS also documents thatdead-letter queueshelp isolate messages that fail processing, which supports the requirement to avoid losing requests. API Gateway can integrate with AWS services, including SQS, to accept requests from mobile and web clients. The ALB options do not provide the same durable buffering, and the SNS option is less appropriate because the key requirement is loose coupling with durable request preservation. Therefore,Bis the strongest design. (AWS Documentation)

The correct approach is to attach anIAM roleto the EC2 instance profile and grant that role permission to read the secret fromAWS Secrets Manager. AWS documentation states that Secrets Manager uses IAM for authentication and authorization, and EC2 instance profiles are the supported mechanism for providing temporary credentials to applications running on EC2 instances. This avoids hardcoded credentials and removes the need for long-term access keys on the server. The other options either misuse IAM users, rely on the wrong access mechanism, or do not reflect how Secrets Manager permissions are normally granted to workloads.

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For latency-sensitive, globally distributed applications such as online gaming, minimizing network latency between users and application endpoints is critical. AWS Global Accelerator is designed specifically for this purpose. It uses the AWS global network and Anycast IP addresses to route user traffic to the closest healthy endpoint, reducing latency and improving performance for users worldwide.

Option C is the best solution because Global Accelerator directs traffic over the AWS backbone network instead of the public internet, which significantly reduces jitter and latency. It also provides built-in health checks and automatic failover, improving availability as the service expands globally. Importantly, Global Accelerator works seamlessly with existing Application Load Balancers, allowing the company to enhance performance without redesigning the application stack.

Option A (CloudFront) is optimized for caching static and cacheable content and is not ideal for real-time, stateful gaming traffic. Option B adds unnecessary API abstraction and additional latency. Option D introduces regional duplication and DNS-based routing, which is slower to react to network conditions and does not provide the same low-latency routing as Global Accelerator.

Therefore, C meets the requirement for global expansion with the least possible latency while maintaining a simple and highly performant architecture.

The correct solution is to create anIAM roleand attach it to the EC2 instance profile. AWS documentation states that applications running on EC2 should use IAM roles so the instance can receivetemporary credentials automaticallythrough the instance metadata service. This avoids embedding long-term access keys in code or on the server. DynamoDB supports IAM and temporary credentials, so the application can use the role’s permissions to access the table securely. EC2 key pairs are only for SSH access and have nothing to do with DynamoDB authorization. IAM users and long-term access keys are less secure and create unnecessary credential management overhead.

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Why Option C is Correct:

NAT Gateway: Allows private subnets to access the internet for outbound requests while preventing inbound connections.

High Availability: Deploying NAT gateways in both AZs ensures fault tolerance.

Shared Route Table: Simplifies routing configuration for private subnets.

Why Other Options Are Not Ideal:

Option A: Creating separate route tables for each subnet adds unnecessary complexity.

Option B: Internet gateways allow inbound access, violating the requirement to block public IPv4 access.

Option D: Egress-only internet gateways are designed for IPv6, not IPv4.

AWS References:

Amazon VPC NAT Gateway:AWS Documentation - NAT Gateway

The production workload is steady and long-running, so a commitment-based discount is appropriate there. Compute Savings Plans are a flexible way to reduce cost for consistent production compute usage. The nonproduction environments run only during weekday business hours and can be shut down when not in use, so using On-Demand Instances and turning them off outside working hours is more cost-effective than reserving or dedicating capacity for them. Dedicated Hosts would be unnecessarily expensive, and reserving nonproduction capacity would waste money on idle time. Therefore, combining Compute Savings Plans for production with shut-down On-Demand nonproduction environments is the most cost-efficient design.

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S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

" S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable. "

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

A warm pool is an Auto Scaling feature that keeps instances in a pre-initialized state so they can quickly join the active group when scaling is required. This reduces the time needed for instance bootstrapping and makes new capacity available almost instantly. Option A only increases capacity limits but does not address slow boot times. Option C merely extends grace periods without solving the delay. Option D forces overprovisioning, which is wasteful and not aligned with cost optimization. Using a warm pool (B) directly addresses the problem by reducing response time to scaling events.

Amazon DocumentDB global clusters support managed cross-region failover, allowing you to promote a secondary region to become the new primary with minimal downtime. This ensures business continuity during maintenance or regional disruptions.

Reference Extract:

" Amazon DocumentDB global clusters support managed cross-Region failover, allowing you to recover quickly from regional disruptions with minimal downtime. "

Source: AWS Certified Solutions Architect – Official Study Guide, DocumentDB and Resiliency section.

Amazon RDS for MySQL supports the creation of read replicas, which are read-only copies of the primary database instance. By offloading read-heavy operations, such as reporting queries, to a read replica:

Performance Improvement: The primary DB instance is relieved from the additional load, reducing the likelihood of timeouts during order processing.

Data Consistency: Read replicas use asynchronous replication, ensuring that they have up-to-date data for accurate reporting.

Scalability: Multiple read replicas can be created to handle increased read traffic.

This approach allows employees to continue running necessary reports without impacting the performance of the ordering application.

AWS Batch supports Windows-based jobs and automates provisioning and scaling of compute environments. Paired with AWS Fargate, it removes the need to manage infrastructure. This solution requires the least operational overhead and is cloud-native, providing flexibility and scalability.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.

Compute Savings Plans apply to EC2, AWS Fargate, and AWS Lambda usage, maximizing coverage for mixed architectures while retaining flexibility across instance families, regions, OS, and tenancy. For private connectivity, Lambda functions must be configured with VPC access to attach ENIs in the target subnets, enabling direct network access to EC2 in private subnets (no public subnets are required for intra-VPC communication). EC2 Instance Savings Plans only discount EC2 usage and are tied to a specific instance family in a region, reducing flexibility and leaving Lambda costs undiscounted. Keeping Lambda in the service VPC prevents direct access to private EC2 without VPC configuration. Thus, a 1-year Compute Savings Plan plus connecting Lambda to the private subnets minimizes total cost and meets connectivity needs.

Amazon DynamoDB supports Time to Live (TTL), which automatically and asynchronously deletes expired items based on a timestamp attribute. TTL is ideal for automatic data expiration with very low operational overhead.

In this scenario:

When a user requests deletion, the system can calculate an expiration timestamp one month in the future.

A Lambda function is used once per request to write or update the item’s TTL attribute to that timestamp (Option C).

DynamoDB TTL then automatically removes the item after the expiration time. Deletion typically occurs within 48 hours of the TTL timestamp, which satisfies the “within one month” requirement.

This approach offloads the actual deletion to DynamoDB and avoids building complex orchestration or periodic cleanup jobs.

Options A, B, and D add unnecessary components (EventBridge, streams, AWS Config, and Lambda-based deletion workflows) on top of TTL or custom logic, increasing operational overhead without providing additional value for the given requirement.

The key requirements are asynchronous processing, high availability, burst handling, and strict message ordering. Amazon SQS FIFO queues are specifically designed to guarantee exactly-once processing and ordered message delivery, making them ideal for transactional workflows like ecommerce order processing.

Option B meets all requirements. SQS FIFO preserves the order of messages within a message group and scales automatically to absorb traffic spikes. Integrating SQS FIFO with AWS Lambda enables serverless, event-driven processing with automatic scaling and no infrastructure management. Lambda processes messages as they arrive while maintaining order guarantees.

Option A (SNS) does not guarantee ordering and is designed for fan-out messaging. Option C (SQS standard) scales well but does not preserve order. Option D introduces unnecessary batch infrastructure and increased latency.

Therefore, B is the best solution because it combines ordered delivery, resilience, elasticity, and serverless compute, ensuring reliable and scalable order processing.

Amazon RDS Custom for Oracle: This service allows you to bring your own Oracle Database licenses and provides the flexibility to customize the database settings, making it suitable for applications that require privileged access and third-party database features.

BYOL (Bring Your Own License):

RDS Custom supports the BYOL model, allowing you to use your existing Oracle licenses and comply with licensing requirements.

This helps in leveraging existing investments and reducing migration costs.

Customization and Third-Party Features:

RDS Custom allows for deeper customization of the database environment compared to standard RDS instances.

This makes it possible to support the third-party features that your application relies on without significant changes.

Migration Process:

Use native Oracle tools like Data Pump or RMAN to migrate the database to RDS Custom.

Customize the database settings post-migration to ensure compatibility with third-party features.

Security groups are stateful, but they cannot explicitly deny traffic — only allow.

Network ACLs are stateless and support explicit deny rules.To prevent Application A from sending traffic to Application B, configure a deny outbound rule in the network ACL of Application A’s subnet to block traffic to Application B’s subnet.

“Unlike security groups, network ACLs support both allow and deny rules, enabling you to explicitly block traffic.”

— Network ACLs

This is the correct method to block outbound traffic between subnets.

The least operational overhead approach is to use AWS Directory Service with AWS Managed Microsoft AD and establish a trust relationship with the existing on-premises Microsoft Active Directory. This pattern lets employees continue using their existing corporate credentials while AWS operates the directory infrastructure (patching, monitoring, availability, and domain controller management), reducing the burden compared to building and maintaining custom federation components. With a trust in place, identities and group memberships from the on-premises directory can be used for authentication and authorization workflows in AWS.

To provide role-based access to AWS resources and the AWS Management Console, users should assume IAM roles that grant permissions based on job function. IAM roles with appropriate trust policies enable temporary credentials and least-privilege access without creating long-lived IAM users for every employee. This aligns with AWS best practices of centralized identity management and using roles for access control.

Option B is not a standard direct integration pattern: IAM does not “LDAP bind” directly to on-premises AD for console access. Option C can work (custom identity broker + STS) but increases operational overhead because the company must build, secure, operate, and maintain the broker, including availability, updates, and incident response. Option D is not the typical solution for employee console federation from AD; Amazon Cognito is primarily aimed at customer identity and application sign-in, not as the primary mechanism for workforce console access to AWS accounts.

Therefore, A provides the required federation with the smallest operational footprint while supporting role-based access control.

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don ' t provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn ' t offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

AWS Security Hub provides a centralized view of security findings across AWS accounts and services. It integrates natively with AWS Config conformance packs, which evaluate compliance against industry standards such as CIS and PCI-DSS.

From AWS Documentation:

“AWS Security Hub aggregates, organizes, and prioritizes security alerts and compliance status across AWS accounts. Use AWS Config conformance packs to assess compliance with security frameworks.”

(Source: AWS Security Hub User Guide – Managing Findings and Compliance)

Why C is correct:

Security Hub provides a centralized dashboard for compliance visibility.

Conformance packs in AWS Config automate compliance checks across accounts.

Fully managed, minimal maintenance, and integrates natively with AWS services.

Why others are incorrect:

A: Conformance packs are not a feature of Amazon Inspector.

B: Third-party tools on EC2 require management and add operational overhead.

D: Systems Manager is not designed for compliance aggregation.

The issue described is a classic case of read-heavy reporting workloads interfering with transactional application traffic. Although the RDS instance is configured for Multi-AZ, Multi-AZ deployments are designed for high availability and failover, not for scaling read performance. All reads and writes still occur on the primary instance, which explains why reporting queries negatively affect customer-facing performance.

Option C, adding an Amazon RDS read replica, is the most effective and AWS-recommended solution. Read replicas asynchronously replicate data from the primary instance and provide a separate endpoint for read-only workloads such as reporting, analytics, and dashboards. By redirecting the finance team’s reporting queries to the replica endpoint, the primary database is freed to handle transactional workloads, significantly improving overall application performance.

Option A (RDS Proxy) helps manage database connections efficiently, especially for bursty workloads, but it does not offload query execution or isolate heavy read traffic. Option B is invalid because RDS Multi-AZ does not support spreading a single DB instance across three AZs, and even if it did, it would not reduce read contention. Option D increases instance resources but does not solve the root cause; reporting queries would still compete with production traffic and would also increase cost unnecessarily.

Therefore, C is the optimal solution because it isolates reporting workloads, scales read capacity, improves application responsiveness, and aligns with AWS best practices for high-performance database architectures.

AWS Storage Gateway file gateway presents a file interface backed by Amazon S3 and supports NFS. This allows local applications to access data via NFS while also enabling cloud applications to use the data stored in S3 for analytics and processing, fulfilling both hybrid and cloud-native requirements.

Reference Extract:

" AWS Storage Gateway file gateway offers NFS and SMB access to data stored in Amazon S3, supporting hybrid workloads for local and cloud access. "

Source: AWS Certified Solutions Architect – Official Study Guide, Hybrid and Storage Gateway section.

CloudTrail log file validation ensures that the log files have not been altered or deleted after delivery, providing an immutable audit log. Using KMS-managed encryption keys for CloudTrail log files adds another layer of data security, and AWS Config can monitor compliance to ensure this security is always enforced.

Reference Extract:

" CloudTrail log file validation provides assurance about the integrity of CloudTrail logs. Using SSE-KMS encryption and monitoring with AWS Config helps secure and audit logs for compliance. "

Source: AWS Certified Solutions Architect – Official Study Guide, CloudTrail Security section.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user ' s geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.

Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.

Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.

AWS Transit Gateway is purpose-built for large-scale, hub-and-spoke network architectures. It simplifies connectivity between multiple VPCs and on-premises environments, which is ideal for managing hundreds of VPCs.

“AWS Transit Gateway enables you to connect your VPCs and on-premises networks through a central hub. This simplifies your network and puts an end to complex peering relationships.”

— Transit Gateway Documentation

Features:

Scales to thousands of VPCs.

Integrates with AWS Direct Connect via Direct Connect Gateway.

Centralized routing control.

Incorrect Options:

A: VPC endpoints are for private access to AWS services—not VPC-to-VPC connectivity.

C: Route 53 is DNS, not a network transport layer.

D: Secrets Manager is for secret storage, not networking.

Amazon Aurora MySQL provides:

Continuous, incremental backups to Amazon S3 with point-in-time recovery (PITR), allowing recovery to any point within the retention window (typically up to 35 days). This easily achieves an RPO of less than 5 hours, often down to seconds.

Support for database auditing parameters so audit logs can be retained and managed (for example, in database logs or external log services) to meet the 7-day audit requirement.

Auto scaling (via read replicas, Aurora Serverless v2, or capacity management) to handle variable read/write demand throughout the year with minimal operational overhead.

Why others are less suitable:

A: DynamoDB is NoSQL and does not directly satisfy typical relational database + SQL audit requirements; Streams also only retain 24 hours, not 7 days, and on-demand backups do not inherently give an RPO < 5 hours without frequent scheduling.

B: Amazon Redshift is a data warehouse, not a primary transactional application database.

C: Snapshots every 5 hours give an RPO of up to 5 hours, not less than 5 hours, and rely on discrete snapshot points rather than continuous PITR.

AWS Step Functions is the recommended service for orchestrating multi-step, long-running workflows with state tracking, retries, and external API calls. It reduces operational overhead by eliminating the need for custom orchestration logic.

API Gateway receiving work items and sending them to SQS (Option E) provides buffering, elasticity, and decoupling, ensuring immediate ingestion regardless of backend load.

Option A forces Lambda to handle long execution paths, which is not optimal for 30-minute tasks and multi-state workflows. Option C triggers Lambda directly without buffering. Option D uses fixed EC2 instances, which does not scale dynamically.

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This is a classic fanout pattern. AWS documentation states that Amazon SNS can fan out messages to multiple subscribers, including Amazon SQS queues. AWS also notes that using SQS with SNS gives each subscriber its own durable queue, which buffers events independently and allows consumers to process at their own pace. That directly satisfies the need for every consumer to receive each order event, to absorb bursts, and to let new consumers be added without changing the producer. A single SQS queue would distribute messages across consumers instead of delivering every event to every consumer. Direct EventBridge targets do not provide the same durable per-consumer buffering described in the question. SNS plus one SQS queue per consumer is the most flexible and resilient design.

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Amazon S3 supports one Lifecycle configuration per bucket that can contain multiple rules. Lifecycle rules can include filters, including object size filters: ObjectSizeGreaterThan and ObjectSizeLessThan. You can combine rules so that one targets large objects and another provides a default expiration. Here, configure Rule 1 with a size filter ObjectSizeGreaterThan = 1 TB and Expiration = 2 days (48 hours) to purge very large videos early. Configure Rule 2 with Expiration = 7 days without a size filter to serve as a catch-all maximum retention for all objects. Options B and D are invalid because S3 does not allow multiple Lifecycle configurations per bucket. Option C cannot distinguish large files; it would delete all files at the same schedule without honoring the 48-hour policy for > 1 TB objects. This single configuration with two rules meets both retention targets with minimal overhead and full S3-native capability.

The correct answer isCbecause the requirement separates encryption into two parts:encryption at restandencryption in transit. Fordata at rest, AWS usesAWS Key Management Service (AWS KMS)to manage encryption keys for services such asAmazon EBSandAmazon Aurora. EBS volumes can be created as encrypted volumes by using KMS keys, and Aurora supports encryption at rest through KMS as well. This protects stored application and database data without requiring the company to build its own key management system.

Fordata in transit, the proper AWS service isAWS Certificate Manager (ACM). AnApplication Load Balancercan use an ACM-issued or ACM-imported SSL/TLS certificate to terminate HTTPS connections and encrypt traffic between clients and the load balancer. This is the AWS standard approach for securing web traffic to internet-facing applications.

Option A is incorrect because it reverses the roles of the services.KMS does not provide certificates for TLS termination on an ALB, andACM does not encrypt EBS or Aurora storage at rest. Option B is incorrect because there is no single account-wide console setting that automatically turns on all encryption for all services in this way, and use of the root account is not an AWS best practice for normal configuration tasks. Option D is incorrect becauseBitLockeris not the appropriate general AWS-native answer for EBS and Aurora encryption, and ALBs do not use KMS keys directly as TLS certificates.

AWS security best practices recommendKMS for encryption at restandACM certificates for encryption in transit. Therefore, optionCis the correct solution.

Option Aallows importing your own encryption keys into AWS KMS, ensuring control over key material.

Option Duses S3 Glacier with SSE-C, where the customer controls the encryption keys, meeting compliance needs.

Option Buses AWS-managed key material, violating the requirement for key material control.

Option C and Eare not fully compliant with the control requirement.

The requirements emphasize high availability across multiple Availability Zones, automatic recovery, and least operational overhead. The simplest way to achieve this for containerized microservices is to use a fully managed container orchestration service with serverless compute. Amazon ECS with the Fargate launch type provides exactly that.

With ECS Fargate, AWS manages the underlying compute infrastructure (no EC2 instances to provision, patch, scale, or secure at the OS level). You define task definitions and services, and ECS schedules tasks across Availability Zones based on your networking configuration (multiple subnets in different AZs). If a container fails, ECS service scheduling automatically replaces it, fulfilling the “recover from failures” requirement. Fargate also integrates with load balancing, CloudWatch logging, and scaling policies, reducing the operational burden of keeping the platform healthy.

Option A (ECS on EC2) is still managed orchestration, but it requires managing the EC2 capacity layer: AMI updates, instance patching, cluster capacity planning, and instance scaling. Option B (EKS with self-managed nodes) typically carries even more operational responsibility because you manage node groups, upgrades, and Kubernetes operational complexity in addition to the underlying instances. Option D is the highest operational overhead because it lacks managed scheduling and self-healing; you would need to build and maintain your own process manager and failover mechanisms.

Therefore, C is the best answer because it provides a managed, multi-AZ capable, self-healing container platform with minimal infrastructure management—matching the operational efficiency goal while maintaining high availability.

To authenticate users using an on-premises Active Directory Federation Service (AD FS), AWS provides the AWS Directory Service AD Connector. AD Connector is a proxy that connects AWS applications to your on-premises Microsoft Active Directory without requiring complex directory synchronization or the need to set up a separate directory in the cloud.

By integrating AD Connector with the Application Load Balancer (ALB), you can authenticate and authorize users using your existing on-premises credentials. This setup allows the ALB to leverage the authentication capabilities of your on-premises AD FS, providing a seamless and secure user experience.

Amazon EKS Anywhere is the best fit because it is specifically designed to run Kubernetes on customer-managed infrastructure in on-premises and edge environments while simplifying cluster lifecycle management. AWS states that EKS Anywhere helps automate cluster management on your own infrastructure, which matches the requirement to keep network connectivity and underlying infrastructure on premises. By contrast, EKS on Outposts moves the platform to AWS Outposts hardware, and the self-hosted options increase operational burden because the company would still manage the Kubernetes platform itself. The key distinction here is that the company wants simpler management without giving up control of its current on-premises environment. EKS Anywhere is the AWS service built for exactly that balance.

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The correct answer isBbecause the company needs tocentrally manage backups across multiple AWS accounts, improvedisaster recovery posture, and protect backups fromaccidental deletion or compromise of an AWS account.AWS Backup policies in AWS Organizationsprovide centralized governance and automation for backup plans across member accounts. By storing copies of backups inadditional AWS accounts, the company gains stronger isolation and protection against account-level compromise.

Cross-account backup copies are especially important when the requirement includes protection from a malicious attack on an AWS account. If backups remain only in the same account, an attacker with sufficient permissions in that account could potentially alter or delete them. Storing backup copies in a separate account provides administrative separation and strengthens recovery options. This is a recognized AWS best practice for improving cyber resilience and recovery assurance.

Option A is incorrect because copying backups to additional Availability Zones within the same account does not address centralized management across Organizations or protect against malicious actions in that account. Option C improves regional resilience but still lacks centralized policy-based management and account isolation. Option D provides centralized policy management and cross-Region resilience, but it does not directly address the requirement to protect against an attack on a single AWS account as effectively ascross-account backup copies.

AWS guidance for enterprise backup strategy emphasizescentralized backup management with AWS Backup and AWS Organizations, pluscross-account copiesfor stronger protection and recovery. Therefore, usingAWS Backup policies in Organizations to store copies in additional AWS accountsis the best solution.

Amazon S3 File Gateway provides a local NFS mount point, which can be used with minimal changes by the legacy application. Files are transparently uploaded to Amazon S3, allowing the web application to access them directly from S3. This is the most cost-effective and operationally simple way to bridge legacy on-premises NFS output with S3, requiring no changes to the batch process.

AWS Documentation Extract:

“With S3 File Gateway, you can provide applications a local file interface to Amazon S3. S3 File Gateway presents a file-based interface (NFS or SMB), allowing you to use S3 as your scalable, durable storage while making files available to legacy applications.”

(Source: AWS Storage Gateway documentation)

A: Outposts is far more costly and complex than needed.

B: Volume Gateway presents iSCSI block storage, not NFS.

C: Requires re-coding the legacy app to use S3 APIs, not NFS.

AWS best practices strongly recommend enabling MFA on the root user, but they also emphasize planning for MFA device loss. AWS allows the configuration of multiple MFA devices for the root user, which provides redundancy and ensures continued access in disaster scenarios. Option B directly addresses the requirement by enabling a backup authentication method without weakening security controls.

Adding multiple MFA devices ensures that if one device is lost, damaged, or unavailable, the organization can still authenticate securely using the secondary device. This approach maintains strong security while eliminating the risk of permanent root account lockout. It also avoids time-consuming recovery processes that require contacting AWS Support and proving account ownership.

Option A is not sufficient because IAM administrator accounts do not replace root access and cannot perform certain root-only actions. Options C and D are not feasible because new administrator accounts or policy changes cannot be made if root access is already lost.

Therefore, B is the correct and AWS-recommended solution to ensure uninterrupted, secure access to the root account while maintaining MFA protection.

The correct answer isBbecause the company needs order processing to beginimmediatelywhen new orders arrive and also needs ahistory of all order processing attemptsin themost cost-effectiveway. Amazon EventBridge is a serverless event bus that can respond to events as they occur, which makes it a strong fit for near real-time order processing. By using anEventBridge event patternand a rule that invokesAWS Lambdawhen a new order event is received, the solution avoids polling and processes orders only when there is actual work to perform.

This event-driven design is more cost-effective than running scheduled checks or maintaining EC2 instances.AWS Lambdaprovides fully managed compute with automatic scaling and charges based on actual execution time.Amazon DynamoDBis a highly scalable and fully managed NoSQL database that is well suited for storing order-processing results and maintaining a durable history of processing attempts with low operational overhead.

Option A is incorrect because invoking Lambda every minute to check for new orders is a polling model, which introduces delay and unnecessary invocations. Option C is incorrect because using an EC2 instance to poll for events creates higher operational cost and overhead. Option D is less appropriate becauseAmazon EFSis not the best storage choice for durable event history or structured tracking of order-processing attempts.

AWS architectural guidance favorsevent-driven, serverless patternsfor asynchronous order processing when immediate response, scalability, and low cost are required. Therefore,EventBridge + Lambda + DynamoDBis the best solution.

AWS PrivateLink enables private connectivity between VPCs and supported AWS or third-party services without exposing traffic to the public internet. It ensures secure and private communications, making it ideal for connecting internal services to SaaS applications hosted in AWS.

AWS Global Accelerator improves the performance and availability of applications by directing user traffic through the AWS global network of edge locations using anycast IP addresses. It reduces latency and jitter for global users accessing applications in a single Region.

Why this works:

Global Accelerator routes user requests to the nearest AWS edge location using AWS’s high-performance backbone network.

It then forwards traffic to the optimal endpoint — in this case, the public API hosted on EC2.

This is much more cost-effective and requires less operational complexity than deploying and maintaining multiple API Gateway endpoints across regions (Option B), or setting up Direct Connect links for every international location (Option A).

Option C requires no application change and is designed specifically for latency improvement and high availability.

???? References:

AWS Global Accelerator Documentation

Use Cases for Global Accelerator

Performance Improvements for Global Users

Lambda supports mounting an Amazon EFS file system inside your function to store larger dependencies beyond the 250 MB layer quota.

EFS automatically encrypts data in transit using TLS.

“You can configure your Lambda function to mount an Amazon EFS file system, enabling your function to access large amounts of data or large dependencies.”

“Amazon EFS automatically encrypts all data at rest and in transit.”

— Lambda with Amazon EFS

This is the least operational overhead approach.

A. Aurora MySQL:Handles OLTP workloads efficiently with built-in replication and auto-scaling capabilities.

B. EC2 with MySQL:Requires heavy manual maintenance and does not scale seamlessly.

C. RDS for MySQL:Limited in auto-scaling compared to Aurora.

D. Redshift:Primarily for OLAP, not suitable for OLTP workloads.

AWS documentation states that the most secure method for granting private S3 access from VPCs is to use gateway VPC endpoints for Amazon S3. Endpoint policies enforce least privilege by allowing only specific IAM roles access to specific S3 buckets. Because users can obtain temporary credentials through IAM roles, no permanent access keys must be distributed.

S3 bucket policies based on CIDR ranges (Option B) are less secure because CIDR-based access is broader and can grant unintended access. Access points (Option C) still rely on public S3 endpoints unless combined with VPC-only access, which is not stated here. Option D exposes S3 to public internet egress and depends on public IPs, violating least-privilege principles.

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Amazon RDS for MySQL supports read replicas that offload read-intensive workloads such as reporting, leaving the primary instance free for write operations.

“You can use Amazon RDS read replicas to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads.”

— Working with Read Replicas

This is the recommended approach for minimizing performance impact on the primary DB instance.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

For Amazon RDS relational databases experiencing read-heavy workloads, AWS best practice is to create read replicas and offload read traffic to those replicas. A read replica:

Uses asynchronous replication from the primary.

Can serve read-only queries, thereby reducing load and query latency on the primary.

Can be used behind an application-level read/write splitting mechanism or via endpoints that direct read traffic to replicas.

Performance Insights (Option A) helps diagnose performance problems but does not itself offload traffic.

DAX (Option B) is a cache for DynamoDB, not RDS.

Kinesis Data Firehose (Option D) is a streaming ingestion service and cannot increase RDS query concurrency.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The company wants to increase security and reduce operational overhead for SQL Server relational databases. The most direct AWS managed service for SQL Server is Amazon RDS for SQL Server, which offloads routine database administration tasks such as provisioning, backups, patching (within managed service controls), monitoring integrations, and high availability management compared with running SQL Server on self-managed EC2.

Choosing Multi-AZ enhances availability and resilience by maintaining a synchronous standby in a separate Availability Zone with automated failover. This improves overall security posture and operational reliability because the platform handles common infrastructure failure scenarios without requiring custom clustering or manual intervention.

For encryption of sensitive data, integrating RDS with AWS KMS allows encryption at rest using KMS keys. Using an AWS managed key is operationally simple while still providing strong encryption controls, auditability, and centralized key management.

Option A still requires managing the database on EC2 (OS patching, SQL Server patching, backups, HA design, monitoring, recovery), which increases operational overhead significantly. Option C is not a database migration approach; S3 plus Macie does not provide a relational SQL Server engine for applications. Option D changes the data model by moving to DynamoDB and does not preserve SQL Server relational capabilities; CloudWatch Logs is not a data security control for database content.

Therefore, B best satisfies the goals: a managed relational database service (RDS for SQL Server) with built-in high availability (Multi-AZ) and encryption at rest via KMS, reducing ongoing operational work while improving security controls.

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

To enforce that IAM users can only access Amazon RDS and no other AWS services, the recommended approach is to use a Deny statement with NotAction. This ensures that all actions are denied except RDS actions. Options A and B do not fully achieve the restriction: A only allows RDS but does not explicitly deny access to other services if another policy grants access; B’s explicit Deny for “*” would override all other permissions, including the intended RDS Allow, which would result in no access at all. Option D with permissions boundaries still allows other attached policies to grant access outside RDS. Therefore, C is the correct approach to enforce RDS-only access.

A. VPC peering:Creates a fully meshed architecture, which is complex to manage for multiple VPCs.

B. Transit gateway:Simplifies network management by connecting multiple VPCs and on-premises networks via a central hub.

C. PrivateLink:Restricts communication to the application endpoint but may not allow full VPC connectivity.

D. ALB with internet exposure:Not secure or specific to private network communication.

AWS Lambda is event driven and “scales automatically with the number of requests,” charging per request and compute duration in milliseconds; you can schedule with Amazon EventBridge rules. Allocating 1 GB memory proportionally increases available CPU, which suits short CPU bursts. For a 10-second, once-per-hour job, Lambda eliminates instance idle time, providing the lowest cost and no servers to manage. Fargate tasks (A) bill per-second with a 1-minute minimum and require container packaging; for a 10-second job, costs are higher than Lambda. Options C and D keep EC2 management overhead and either maintain instances or add start/stop orchestration complexity; EC2 costs dominate because the instance sits idle for 59+ minutes each hour. Therefore, Lambda + EventBridge is the most cost-effective and operationally simple solution.

ElastiCache for Redis OSS supportsMulti-AZ with automatic failover, which is exactly the feature required here. AWS documentation states that when Multi-AZ is enabled and the primary node fails, ElastiCache automatically promotes a read replica to primary, minimizing downtime and restoring write capability without requiring manual promotion steps. Backups are useful for recovery, but they do not provide automatic operational failover. A design that depends on manual promotion is also weaker than native automatic failover. Since the question explicitly asks for a solution that canautomatically fail over, the correct answer is to enableMulti-AZfor the replication group.

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AWS Secrets Manager is a fully managed service specifically designed to securely store and automatically rotate database credentials, API keys, and other secrets. Secrets Manager provides built-in integration with Amazon RDS for automatic credential rotation on a configurable schedule without requiring downtime. It also manages the secure distribution of the credentials to authorized services, such as your web servers, using IAM policies. Manual solutions (S3, files, cron jobs) do not provide the same level of automation, audit, or security.

Reference Extract from AWS Documentation / Study Guide:

" AWS Secrets Manager enables you to rotate, manage, and retrieve database credentials securely. It supports automatic rotation of secrets for supported AWS databases without requiring application downtime. "

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Secrets Management section.

The issue is most likely that messages become visible in the queue again before Lambda finishes processing them. AWS documents that when Lambda polls SQS, messages remain hidden only for the queue’s visibility timeout, and if they are not deleted before that timeout expires, they can be retrieved again and reprocessed. AWS specifically recommends setting the SQS visibility timeout to at least six times the Lambda function timeout, and if a batch window is used, to add that value as well. Long polling reduces empty receives but does not prevent duplicate processing. Changing to FIFO is unnecessary here, and deleting messages before successful processing risks data loss.

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Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

Amazon SQS FIFO queues are built for workloads that require both message ordering and exactly-once processing behavior. AWS documentation states that FIFO queues preserve first-in, first-out order and support deduplication so duplicate messages are not introduced within the deduplication interval. That directly satisfies the requirement for exact order and exactly-once semantics. SNS alone is a pub/sub notification service, but it does not by itself provide this exact combination as simply as an SQS FIFO queue. Standard queues allow at-least-once delivery and can deliver messages out of order, so they do not meet the requirement. MSK could be engineered to solve the problem, but it introduces far more operational overhead than a managed queue service. For the most operationally efficient serverless design, SQS FIFO is the correct answer.

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Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

Compute Savings Plansoffer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, usingOn-Demand Instancesensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS ' s cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

The correct answer isProvisioned IOPS SSD io2 Block Express. AWS documents that io2 Block Express is recommended for I/O-intensive, latency-sensitive workloads and is designed for consistent sub-millisecond latency with very high IOPS limits. AWS also notes that many instance types can achieve up to32,000 IOPS, which matches the baseline requirement in the question. S3 is object storage and not suitable for a transactional MySQL database. EFS is a shared file system, not the preferred low-latency block storage for this database pattern. gp3 is versatile and cost-effective, but it is not the targeted choice when the requirement explicitly emphasizes sustained, low-latency 32,000 IOPS performance. (AWS Documentation)

The problem is that documents are stored on instance-local storage, so once the site scales horizontally, users may land on an instance that does not have the requested file. Amazon EFS is the best fit because AWS documents that EFS provides shared file storage with concurrent access from multiple EC2 instances and is accessible from all Availability Zones in the Region. That lets every web server read the same document set without changing to an object-storage pattern. The cron-based answers introduce delay and inconsistency, and S3 would require a bigger application-path change than the shared file system approach. For the least disruptive and most reliable restoration of access across multiple EC2 instances, mounting a single EFS file system on all instances is the right solution.

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AWS Shield Advanced provides:

Advanced DDoS detection and mitigation

24/7 access to the AWS DDoS Response Team (DRT)

Real-time metrics and alerts via CloudWatch

Integrated with CloudFront, ALB, Route 53, and Global Accelerator

“Shield Advanced provides enhanced detection and mitigation for more sophisticated DDoS attacks and gives you access to the AWS DDoS Response Team (DRT).”

— AWS Shield Advanced Overview

Incorrect Options:

A (AWS WAF): For application-layer filtering only.

B (Shield Standard): Basic protection, no DRT or attack visibility.

C (Macie): Used for discovering sensitive data in S3, unrelated to DDoS.

The best solution to enforce encryption at rest for Amazon EBS volumes is to use anIAM policyto restrict the creation of unencrypted volumes. To automatically identify and remediate unencryptedvolumes, you can useAWS Configrules, which continuously monitor the compliance of resources, andAWS Systems Managerto automate the remediation by encrypting existing unencrypted volumes. This setup requires minimal administrative overhead while ensuring compliance.

Option B (KMS): KMS is for managing encryption keys, but Config and Systems Manager provide a better solution for automatic detection and enforcement.

Option C (Macie): Macie is for data classification and is not suitable for this use case.

Option D (Inspector): Inspector is used for security vulnerabilities, not encryption compliance.

AWS References:

AWS Config Rules

AWS Systems Manager

Option Ais the simplest solution, using DynamoDB Streams and Lambda for real-time ingestion into S3.

Options B, C, and Dadd unnecessary complexity with Data Firehose or Kinesis.

DAX does not support enabling encryption at rest on an existing cluster. To use encryption at rest, you must create a new DAX cluster with encryption enabled at creation time and migrate workloads accordingly.

AWS Global Accelerator is designed to improve the availability and performance of applications with global users by using the AWS global network. It provides static anycast IP addresses and routes user traffic over the AWS edge network to the optimal AWS Region and endpoint based on health, geography, and routing policies. Global Accelerator supports both TCP and UDP traffic and can have Network Load Balancers as endpoints.

For latency-sensitive workloads such as multiplayer gaming, Global Accelerator reduces latency and jitter compared to internet-based routing and handles Regional failover quickly.

CloudFront (Option A) is optimized for HTTP/HTTPS content caching and is not appropriate for arbitrary TCP/UDP gaming traffic. Application Load Balancers (Option B) do not support UDP traffic. API Gateway (Option D) is for HTTP APIs and is not suitable for raw TCP/UDP game traffic.

The requirement is for immediate processing of uploaded videos and prompt notification to users. According to AWS best practices for event-driven architectures, using S3 event notifications to trigger a Lambda function upon an object creation (upload) is the optimal solution for real-time processing. Lambda can process the file as soon as it is uploaded, ensuring low latency. Once processing is complete, Lambda can save the processed file back to S3 and use Amazon SNS to notify the user.

This approach uses managed services with minimal operational overhead, is scalable, and ensures event-driven processing with instant user feedback. AWS Amplify primarily facilitates application development and hosting but does not natively provide direct push notification support for this backend workflow; instead, SNS is designed for such notification scenarios.

Reference Extract from AWS Documentation / Study Guide:

" Amazon S3 can publish events to AWS Lambda when objects are created. AWS Lambda runs code in response to events and can interact with other AWS services. Amazon SNS is a flexible, fully managed pub/sub messaging and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients. "

Source: AWS Certified Solutions Architect – Official Study Guide, Event-driven Architectures section; AWS Lambda Developer Guide (S3 triggers); Amazon SNS User Guide.

Explanation (AWS Docs):

Although the question says “before sending it,” AWS best practice for sensitive data is SSE-KMS (Server-side encryption with AWS KMS keys), which gives full key usage auditing. It integrates with AWS KMS and provides compliance-friendly encryption at rest automatically.

“SSE-KMS uses AWS Key Management Service to manage encryption keys. SSE-KMS also provides an audit trail of key usage.”

— Protecting Data Using Server-Side Encryption

Why not D?

Client-side encryption requires custom key management and adds operational overhead. C is simpler and compliant.

To ensure high availability and scalability, the web application should run in anAuto Scaling groupacross two Availability Zones behind anApplication Load Balancer (ALB). The database should be migrated toAmazon RDSwithMulti-AZ deployment, which ensures fault tolerance and automatic failover in case of an AZ failure. This setup minimizes administrative overhead while meeting the company ' s requirements for high availability and scalability.

Option A: Read replicas are typically used for scaling read operations, and Multi-AZ provides better availability for a transactional database.

Option B: Replicating across AWS Regions adds unnecessary complexity for a single web application.

Option D: EC2 instances across three Availability Zones add unnecessary complexity for this scenario.

AWS References:

Auto Scaling Groups

Amazon RDS Multi-AZ

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

SSE-KMS (Server-side encryption with AWS Key Management Service) not only encrypts data at rest but also integrates with AWS CloudTrail to provide detailed logs of key usage — meeting the audit requirement.

“SSE-KMS provides the ability to audit key usage to see who used the key and when, via AWS CloudTrail.”

— Amazon S3 Encryption Documentation

Benefits:

Encryption with customer-managed or AWS-managed KMS keys

Audit trails of key usage events

Fine-grained access control

Incorrect Options:

A: SSE-S3 does not support auditing of key usage.

C: SSE-C does not integrate with CloudTrail or KMS.

D: Self-managed keys require external key infrastructure and custom audit logging.

Amazon EFS is the best fit because it is a fully managed, elastic file storage service for Linux workloads, and it scales automatically without pre-provisioning capacity. AWS documentation states thatElastic throughputautomatically scales throughput up or down for spiky or unpredictable workloads, which matches the varying access pattern in the question. AWS also documents thatEFS lifecycle managementandEFS Intelligent-Tieringautomatically transition files between storage classes based on access patterns, which provides the native tiering requirement. FSx for Windows is not the right operating-system fit, and using EC2 plus EBS would require more manual storage management. Therefore,EFS with Elastic throughput and Intelligent-Tieringis the most cost-effective managed design. (AWS Documentation)

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Option B (Amazon FSx for Lustre): FSx for Lustre is optimized for high-performance file systems required by HPC workloads, scaling to millions of IOPS and supporting parallelized data access.

Option E (Cluster Placement Group with Auto Scaling): A cluster placement group ensures low-latency communication between EC2 instances, critical for HPC workloads. Amazon EMR simplifies running large-scale analytics jobs.

Amazon FSx for Lustre Documentation,AWS Placement Groups Documentation

AWS documents that Lambda@Edge can customize CloudFront behavior at the edge and that CloudFront can change the origin domain name based on the viewer-country header. AWS also documents that CloudFront can route to origins based on request headers and other request characteristics. That makes CloudFront with Lambda@Edge the best fit for selecting the Region-specific S3 origin closest to users in Asia and Europe. ALB is not the right service for S3-origin geolocation routing, and plain CloudFront cache behaviors alone do not perform this origin selection logic by viewer location without the edge customization. Therefore, Lambda@Edge plus CloudFront multi-origin selection is the correct design.

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Amazon SQS is the best messaging layer here because it providesdurable bufferingand decouples microservices so they can scale independently while absorbing bursts without losing data. AWS recommends SQS for decoupled architectures where producers and consumers operate at different speeds. For compute,AWS Fargatereduces operational overhead because AWS manages the underlying servers, patching, and capacity layer. API Gateway and WebSockets are not the primary answer for internal asynchronous microservice communication, and SNS is fanout-oriented rather than the most natural queueing model for durable work processing between services. Therefore, SQS plus ECS on Fargate is the most appropriate low-overhead design.

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AWS documentation states that EC2 Auto Scaling is the recommended, cost-effective, and fault-tolerant method to manage workloads with predictable or varying demand. Auto Scaling automatically adds instances during peak traffic and terminates them when demand is low, reducing compute cost while maintaining availability across multiple Availability Zones.

Reserved Instances (Option B) would force the company to pay for peak capacity all day, which is not cost-effective.

Stopping instances manually (Option C) or vertically scaling instances (Option D) reduces fault tolerance and increases operational overhead.

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The correct answers areA, C, and Ebecause the company needs a fully managed solution forSFTP uploads to Amazon S3, followed byautomatic decryptionandmovement of files to another directorywithminimal operational overhead.AWS Transfer Familyis the best fit because it provides a managed SFTP endpoint directly integrated with Amazon S3. Configuring the S3 bucket as the home directory enables customers to upload files without the company needing to manage its own file transfer servers.

The requirement to avoid separate authentication services and minimize operational work is well served by nativeAWS Transfer Family workflows. A workflow can automatically run post-upload steps on files as they arrive. TheDECRYPTaction is specifically designed to decrypt uploaded encrypted files as part of the managed workflow. After decryption, theCOPYaction can place the processed file into the second directory in the same S3 bucket for downstream processing.

Option B is less appropriate because using S3 events and Lambda adds custom orchestration where a native Transfer Family workflow already handles the need more simply. Option D is incorrect because polling with an external script introduces unnecessary infrastructure and operational overhead. Option F is also incorrect because AWS Batch polling and custom decryption logic is far more complex than a managed file-transfer workflow.

AWS best practices favor managed services and native workflow features to reduce custom code and infrastructure management. Therefore, the best solution is to useAWS Transfer Family for SFTP uploads, along withTransfer Family workflow DECRYPTandCOPYactions to automate the full post-upload process.

In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.

Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.

Other Options:

Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn ' t leverage the existing replicas.

Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.

Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.

AWS References:

Amazon Aurora Endpoints- Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.

Custom Endpoints in Amazon Aurora- Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

The most secure and manageable way to provide developers with temporary, least-privilege access is by using AWS IAM Identity Center (formerly AWS SSO). IAM Identity Center allows assigning IAM roles with scoped permissions based on the developer’s team role. This ensures no permanent credentials are required and minimizes risk.

Option B enables role-based access with centralized identity and access management, making it the most secure and scalable solution for managing developer permissions.

To enable DNS resolution from AWS VPCs to on-premises DNS servers over Direct Connect or VPN, AWS recommends using Amazon Route 53 Resolver with outbound endpoints. An outbound endpoint allows DNS queries originating in the VPC to be forwarded to a customer-managed DNS server (e.g., on-prem).

Next, a forwarding rule (Forward type) must be created to forward DNS queries for the custom domain internal.example.com to the on-premises DNS IP addresses. This rule defines what domain names are forwarded and to which DNS servers.

Finally, the rule must be associated with all workload VPCs to allow those VPCs to use the rule. There is no need to deploy endpoints in every VPC — one outbound endpoint is sufficient and can be shared across VPCs via rule association.

???? Reference:

Route 53 Resolver Endpoints

Best practices for hybrid DNS resolution

The workload described is event-driven, low volume, and sporadic, making serverless architecture the most cost-effective choice. AWS Lambda, when triggered by S3 event notifications, runs only when new photos are uploaded and incurs cost only for execution time.

Option C uses S3 event notifications to invoke a Lambda function whenever a new object is created. The Lambda function generates a thumbnail and uploads it to a second S3 bucket. This solution requires no servers, no scheduling, and no idle compute costs, making it extremely cost-efficient for 150 uploads per day.

Options A and B involve running long-lived compute resources that would remain idle most of the time, resulting in unnecessary cost. Option D is invalid because S3 Storage Lens is designed for storage analytics and reporting, not event-driven processing.

Therefore, C is the most cost-effective and operationally efficient solution.

Comprehensive Explanation:Deploying the frontend as a CloudFront distribution with multiple origins provides an efficient and scalable solution. Using WAF rules with CloudFront protects against web vulnerabilities, while the multi-origin configuration allows traffic routing to the on-premises backend APIs. This approach minimizes operational overhead compared to managing EC2 instances.

Amazon S3 Glacier Flexible Retrieval is a low-cost archival storage class that supports retrieval of data within minutes (expedited access ~1–5 minutes), making it ideal for audit scenarios where occasional, quick access to archived data is required. In contrast, Glacier Deep Archive takes hours to retrieve.

S3 Glacier Flexible Retrieval is designed for long-lived archive data that is accessed infrequently and can be retrieved in minutes to hours, making it a strong cost-effective choice for medical records that are rarely accessed. AWS documentation states that standard retrievals from this storage class typically finish in 3 to 5 hours, which fits the requirement to retrieve records within a few hours. For immutability, AWS S3 Object Lock provides a WORM model and can prevent deletion or overwriting for a fixed retention period. Together, Glacier Flexible Retrieval and Object Lock satisfy the needs for low-cost archival storage, long retention, immutability, and acceptable restore times. S3 Standard and Intelligent-Tiering cost more than necessary for rarely accessed 10-year data, and One Zone-IA does not provide the same resilience posture.

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Option Bis the correct solution because:

AWS Security Token Service (STS)allows the web application to request temporary security credentials that grant access to AWS resources. These temporary credentials are secure and short-lived, reducing the risk of misuse.

Using STS and IAM roles ensures scalability by enabling the application to dynamically assume roles with the required permissions for each AWS service.

Option A:Assigning IAM roles directly to instances is less flexible and would grant the same permissions to all applications on the instance, which is not ideal for a multi-service web application.Option C:AWS IAM Identity Center is used for managing single sign-on (SSO) for workforce users and is not designed for granting programmatic access to web applications.Option D:Storing long-term access keys, even in AWS Systems Manager Parameter Store, is less secure and does not scale well compared to temporary credentials from STS.

AWS Documentation References:

AWS Security Token Service (STS)

IAM Roles for Temporary Credentials

The workload is Windows-based and requires a shared file system with SMB support and very high throughput for 4K video editing.

Amazon FSx for Windows File Server is a fully managed, highly available native Windows file system that supports the SMB protocol, Active Directory integration, and can deliver high throughput and low latency suitable for media workloads.

FSx for Windows File Server supports automatic storage scaling to grow file system capacity as data increases, matching the requirement of +1 TB per week with minimal admin effort.

A Multi-AZ SSD deployment provides high availability and performance.

Why others are not correct:

B: Amazon EFS is NFS, not SMB, and is optimized for Linux clients, not Windows-based environments.

C: FSx for Lustre is a high-performance POSIX file system typically used with Linux-based HPC workloads, not Windows + SMB.

D: S3 File Gateway is not designed for sustained multi-GB/s shared-edit performance and adds additional complexity; it is more suited for backup/archival and limited shared file access.

The data isfrequently accessed only for the first 30 daysand then israrely accessedbut must be retained for7 years. That pattern matchesAmazon S3 Standardfor the initial active period and thenS3 Glacier Deep Archivefor long-term retention at the lowest storage cost. AWS guidance positions Glacier Deep Archive for long-lived archive data that is accessed once or twice a year and is ideal for compliance-style retention. EFS and EBS are not cost-effective archival storage choices for this use case. S3 Standard-IA is cheaper than S3 Standard, but Glacier Deep Archive is more cost-optimized for multi-year rare access.

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Amazon EMR allows the creation of security configurations to specify settings for encrypting data at rest, data in transit, or both. These configurations can be applied to clusters to ensure that data stored in Amazon S3, local disks, and data moving between nodes is encrypted.

By creating and applying an EMR security configuration, the company can ensure that all data processing complies with encryption requirements for sensitive patient data.

The correct answer isCbecause the workload is abatch processing jobthat runs during a limited time window each night and can tolerate interruptions. The key statement is thatif a job fails on one instance, another instance will reprocess the job. That means the workload is fault-tolerant and well suited forAmazon EC2 Spot Instances, which provide unused EC2 capacity at a much lower cost than On-Demand Instances.

Batch jobs, big data processing, background analytics, and other interruptible workloads are classic use cases for Spot Instances. Because the instances run only from12:00 AM to 06:00 AMand do not need to be available continuously, committing to a 1-year Savings Plan or Reserved Instances would not be the most cost-effective approach. Those options are better for steady and predictable long-running usage, not for short-duration nightly workloads.

Using a new launch template configured for Spot Instances allows the Auto Scaling group to launch lower-cost capacity for the processing window. Scaling based on CPU usage can help the group add or remove instances according to processing demand, improving both elasticity and cost efficiency.

Option A is less cost-effective because Savings Plans are more suitable for consistent usage over time. Option B is also less cost-effective because Reserved Instances lock in a commitment for capacity that is used only a few hours each day. Option D does not address the cost model and could even increase cost by using larger instances.

AWS cost optimization guidance recommendsSpot Instances for fault-tolerant, flexible, interruption-tolerant workloads, making optionCthe best answer.

API Gatewayresource policiesare the native way to control who can invoke a REST API, including allowing access from specified AWS accounts. AWS documentation says resource policies can be attached to an API to control whether specified principals can invoke it, and it explicitly lists users from a specified AWS account as a supported use case. Private APIs are callable through interface VPC endpoints, but the primary authorization control for which external accounts may invoke the API is the API’s resource policy. Endpoint policies can add controls on the VPC endpoint side, but the clean cross-account access control for the API itself is the resource policy.

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AWS Transit Gateway is the correct architecture for connecting a very large number of VPCs in a scalable hub-and-spoke model. VPC peering between more than 1,000 VPCs would create an unmanageable mesh of peering connections and route table updates. VPN tunnels are intended for encrypted connectivity over external networks, not for efficient VPC-to-VPC communication at this scale. A Direct Connect gateway is used for connecting on-premises networks to AWS and is not the right mechanism for attaching every VPC through public VIFs. Transit Gateway supports VPC attachments and centralized route tables, which makes it the operationally scalable and high-performing choice for communication among many VPCs.

VPC Endpoint for S3: A gateway endpoint for Amazon S3 enables you to privately connect your VPC to S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Configuration Steps:

In the VPC console, navigate to " Endpoints " and create a new endpoint.

Select the service name for S3 (com.amazonaws.region.s3).

Choose the VPC and the subnets where your EC2 instances are running.

Update the route tables for the selected subnets to include a route pointing to the endpoint.

Security Compliance: By configuring an S3 gateway endpoint, all traffic between the VPC and S3 stays within the AWS network, complying with the company ' s security regulations to avoid internet traversal.

To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

To send data privately from on-premises to S3 buckets across multiple AWS accounts:

A: Using AWS Direct Connect with a private virtual interface (VIF) enables private connectivity from on-premises into the VPC.

E: After establishing the central VPC in a networking account, use VPC peering to allow access to S3 buckets across multiple AWS accounts.

“Direct Connect private virtual interface (VIF) allows private IP connectivity to VPC resources.”

“VPC Peering enables routing of traffic between VPCs using private IP addresses.”

— AWS Direct Connect Documentation

— VPC Peering Guide

Why not others:

B: Public VIF sends traffic over public AWS services—not fully private.

C: Interface endpoint is useful within a single account/VPC context.

D: Gateway endpoint doesn ' t solve inter-account routing.

Private subnets require outbound internet access to download updates, but they must not have public IPs or direct inbound access.

The recommended AWS solution is to create a NAT gateway in a public subnet. Private subnet route tables are updated to route internet-bound traffic (0.0.0.0/0) to the NAT gateway. The NAT gateway then uses the internet gateway attached to the VPC to communicate with the internet.

Option B (NAT instances) is an older approach and less scalable/maintainable than NAT gateways. Option C (egress-only internet gateway) is for IPv6 outbound-only traffic, not IPv4. Option D is invalid because NAT gateways must be deployed in public subnets.

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.

To grant cross-account access to an Amazon S3 bucket, you can use a bucket policy that specifies the AWS account ID of the account you want to grant access to. This method allows Account B to access the S3 bucket in Account A without the need for additional services or configurations.

Enabling Multi-AZ deployment for an Amazon RDS DB instance is the simplest and most effective way to improve database availability and eliminate a single point of failure. Multi-AZ deployments provide automatic failover to a standby instance in case of a failure of the primary instance. This approach is managed by AWS and only requires a modification to the existing instance, with minimal manual intervention or downtime, thus providing the least implementation effort.

Reference Extract from AWS Documentation / Study Guide:

" With Multi-AZ deployments, Amazon RDS automatically creates a primary DB Instance and synchronously replicates the data to a standby instance in a different Availability Zone (AZ). In case of a failure of the primary DB Instance, Amazon RDS automatically fails over to the standby so that database operations can resume quickly. "

Source: AWS Certified Solutions Architect – Official Study Guide, RDS High Availability section.

AWS Site-to-Site VPN is a cost-effective way to securely connect your on-premises data center with AWS resources. In this scenario:

Applications in private subnetsrequire access to the API hosted in the on-premises data center.

ASite-to-Site VPN connectionis a secure and cost-efficient option to route traffic between the VPC and on-premises resources.

Transit GatewayandPrivateLinkare not cost-effective for this use case.

NAT Gatewayonly provides internet access for private subnets, which is not suitable for reaching an on-premises resource.

AWS Documentation Reference:

AWS Site-to-Site VPN

AWS Transfer Family (SFTP) allows clients to use standard SFTP clients and SSH keys without changes. By enabling service-managed users, clients can continue uploading files with their existing tools.

The service delivers the files directly into S3, reducing latency between upload and processing. This removes the need for EC2, custom scripts, and periodic transfers. It fully meets the requirement for a managed solution with minimal disruption to client processes.

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3,Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.

Why Other Options Are Incorrect:

Option B:EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.

Option C:While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.

Option D:Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.

Key AWS Features Used:

AWS Lambda:Real-time serverless processing with integration capabilities for Aurora and S3.

Amazon S3:Cost-effective storage for enriched data.

Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

AWS Documentation References:

AWS Lambda Function Overview

Amazon Redshift Spectrum

Processing Streaming Data with Kinesis Data Streams

The requirement that the security team manage key lifecycle actions such as creation, deletion, and rotation points tocustomer managed AWS KMS keys, not SSE-S3. Customer managed KMS keys allow fine-grained IAM authorization for both the Lambda execution role and the security administrator group. That supports least privilege because the Lambda role can be restricted to decrypt usage, while the administrator group can be limited to the KMS management actions it actually needs. The other answers either do not provide the required KMS management control or grant permissions too broadly. Therefore, using customer managed KMS keys with separate, scoped IAM permissions for decryption and key administration is the correct design.

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Amazon ECS with Application Auto Scaling, an Application Load Balancer, and CloudFront is the strongest architecture because it supports scale, high availability, and lower operational overhead while allowing the company to reuse the existing application code through containerization. The ALB distributes traffic across ECS tasks and supports health checks. ECS Service Auto Scaling automatically adjusts running tasks based on demand, allowing the application to handle millions of requests. CloudFront improves global delivery by caching content and routing users through edge locations. A self-managed Kubernetes cluster in one Availability Zone is not highly available and creates unnecessary management work. API Gateway and Lambda would require application redesign. Keeping the origin on premises does not meet the migration goal.

AWS documentation states that the correct and least-privilege method for cross-account SNS-to-SQS integration is:

• Add specific SNS topic ARNs to the SQS queue policy.

• Allow only those topics to publish messages to the queue.

• Ensure SNS has permission to publish to the specific queue ARN.

This ensures strict scoping and adheres to least privilege.

Options A and D grant overly broad permissions. Option B allows publishing to any queue, which violates least privilege.

Amazon Aurora MySQL supports the SELECT INTO OUTFILE S3 SQL syntax to export query results directly to Amazon S3. This is an efficient and low-overhead method for archiving data.

Once data is in S3, Lifecycle rules can be configured to automatically transition older data to lower-cost storage classes (such as S3 Glacier) or delete it after a defined period, providing a cost-optimized and automated archive solution.

The Glue-based options involve more services and operational overhead. SCT is intended for database migrations, not for periodic data archival.

AWS Config has a built-in managed rule (access-keys-rotated) that evaluates IAM access key age. Config rules detect non-compliant resources automatically, without building custom logic.

After Config identifies old keys, EventBridge can trigger an AWS Lambda function to disable and delete the keys. Lambda provides a fully managed compute layer requiring no servers or batch environments.

Using AWS Batch (Options A, B, and D) adds unnecessary operational overhead for a simple automation task.

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As the number of VPCs grows, managing individual VPC connections becomes complex and unscalable. AWS Transit Gateway is specifically designed to act as a central hub that connects multiple VPCs and on-premises networks through Direct Connect.

Option B simplifies network architecture by allowing hundreds or thousands of VPCs to connect through a single gateway, reducing routing complexity and operational overhead. Transit Gateway supports scalable routing, centralized inspection, and simplified expansion.

The other options do not address network connectivity or scaling challenges. Therefore, B is the correct solution.

Amazon SQS FIFO queues guarantee the order of message delivery and ensure that each message is delivered exactly once. Paired with AWS Lambda, this creates a scalable, fault-tolerant architecture that processes each order in order and prevents duplicates, which is critical for ecommerce workflows.

Using Amazon SQS decouples the API from the database, allowing the system to handle write bursts without data loss. The queue buffers incoming requests, and AWS Lambda processes them asynchronously, writing to the RDS instance at a sustainable rate.

From AWS Documentation:

“Amazon SQS acts as a buffer between components that produce and consume data, allowing each to operate independently. You can use AWS Lambda to process messages from SQS and write them to other services such as Amazon RDS.”

(Source: Amazon SQS Developer Guide)

Why C is correct:

SQS absorbs write surges and ensures durable message retention.

Lambda scales automatically with message volume and reduces DB connections.

Prevents timeout errors by smoothing traffic spikes.

Why others are incorrect:

A: Scaling RDS vertically doesn’t address connection spikes.

B: Multi-AZ is for availability, not write throughput.

D: SNS is for fan-out message delivery, not queue-based buffering.

Encryption at Rest: AWS Key Management Service (AWS KMS) provides centralized control over the cryptographic keys used to protect data. By using AWS KMS with Amazon S3, you can manage encryption keys and define policies to control access to them.

Encryption in Transit: By enforcing the aws:SecureTransport condition in the S3 bucket policy, you ensure that all data is transmitted over HTTPS, protecting data in transit.

Access Control: IAM policies allow you to define fine-grained permissions for users and roles, ensuring that only authorized entities can access the customer records.

Monitoring: AWS CloudTrail provides a record of actions taken by a user, role, or AWS service in Amazon S3, enabling you to monitor access to the records.

The requirements combine long-term, low-cost storage with retrieval within minutes, which narrows the viable storage classes significantly. Amazon S3 Glacier Flexible Retrieval is designed for exactly this use case: infrequently accessed data that must still be retrievable quickly.

Option C provides substantially lower storage cost than EBS snapshots or EFS while supporting expedited retrievals, which can return data within minutes when required for audits. This meets both the cost and retrieval time constraints. Glacier Flexible Retrieval also integrates seamlessly with S3 lifecycle policies, enabling automated transitions from EBS snapshots or S3 Standard to archival storage.

Option A (EBS snapshots) provides durability but is more expensive for long-term storage at this scale. Option B (Glacier Deep Archive) is cheaper but does not meet the retrieval requirement, as restore times are measured in hours, not minutes. Option D (EFS) is a high-performance file system and is the most expensive option for long-term backups.

Therefore, C is the correct choice because it balances cost efficiency, durability, and audit-friendly retrieval times, aligning with AWS storage best practices for compliance-driven archival data.

AWS Outposts extends AWS infrastructure and services to on-premises locations. Running Amazon EMR on Outposts allows for processing data that resides locally while benefiting from the managed services of EMR. This enables compliance with data residency requirements and provides scalability and manageability for analytics.

Amazon EBS Snapshots Recycle Bin enables you to specify retention rules for EBS snapshots based on tags. When snapshots are deleted, they are retained in the Recycle Bin for a specified duration, preventing accidental deletion. Tag-level rules allow selective protection without changing IAM roles or user permissions.

A. Mutual TLS:Provides secure communication but does not integrate with AWS credential exchange.

B. AWS Signature v4:Requires direct integration with AWS and is less secure for external systems.

C. Kerberos:Not natively supported for AWS API authentication.

D. IAM Roles Anywhere:Enables AWS API access using X.509 certificates without long-term credentials.

The correct answer isDbecause the application peak hours occur at thesame time each day, and the performance issue happens specifically at thestart of peak hours. This indicates that the company knows in advance when additional capacity will be needed. Ascheduled scaling policyis the most appropriate solution because it allows the Auto Scaling group to increase capacitybeforedemand rises, ensuring that instances are already launched and available when users begin accessing the application.

Dynamic scaling based on CPU or memory reacts onlyafterutilization increases. Because launching EC2 instances takes time, reactive scaling can lead to delays and degraded user experience at the beginning of peak periods. That directly matches the problem described in the question, where performance is slow at the start but normalizes later once capacity catches up. Scheduled scaling solves this by making capacity available proactively.

Option A is incorrect because an Application Load Balancer distributes traffic among existing instances but does not add capacity ahead of predictable demand. Option B is incorrect because Auto Scaling does not natively scale on memory utilization unless a custom metric is configured, and even then it would still be reactive rather than proactive. Option C is also incorrect because CPU-based dynamic scaling reacts after demand increases and therefore may not prevent the startup lag during peak traffic.

AWS design guidance recommends usingscheduled scalingwhen traffic patterns are predictable and occur at known times. This approach improves application responsiveness and user experience by making sure enough EC2 capacity is available before the workload surge begins.

Amazon CloudFront is a highly cost-effective CDN that caches content like images and videos at edge locations globally. This reduces latency and the load on the origin S3 bucket. It is ideal for static content that is accessed by many users.

Amazon S3 Standard provides virtually unlimited scalability, high durability (11 nines), and millisecond latency. It is designed for storing large volumes of unstructured content such as media files. S3 also supports pre-signed URLs and direct browser uploads, enabling users to upload files securely without passing through backend servers. EBS volumes (A) are block storage, limited to single AZ, and not suitable for web-scale storage. EFS (B) is a shared file system for POSIX workloads, not for direct browser uploads. S3 Express One Zone (D) offers higher performance for small objects but does not provide cross-AZ durability, making it unsuitable for growing global content. Therefore, option C is the most scalable, durable, and cost-effective solution.

Amazon EFS is the correct solution because the application already expects a shared Linux file system and cannot be modified during migration. EFS supports the NFS protocol, can be mounted by multiple EC2 instances in an Auto Scaling group, and scales automatically as files are added or removed. Mount targets in multiple Availability Zones provide high availability for the shared file system. Because the documents are stored once and retrieved later for reference, EFS Standard-IA reduces storage cost for infrequently accessed files while preserving file-system access semantics. A self-managed NFS server on EC2 creates a single point of failure and management burden. S3 and SFTP do not preserve the existing NFS shared-file-system behavior without application changes.

SageMaker AI batch transform is the best option because the company runs inference only once each week on large datasets stored in Amazon S3. Batch transform is designed for offline batch inference, so the company does not need to deploy or maintain a persistent real-time endpoint. The same managed SageMaker workflow can process input records, run the model, and write inference results back to S3 with minimal infrastructure management. AWS Batch and Amazon EMR can process large datasets, but they introduce extra services and operational responsibility. AWS Glue is useful for ETL, but the option still incorrectly uses a SageMaker processing job for the inference workload rather than the managed batch inference feature. Therefore, batch transform provides the least operational overhead.

AWS Shield Advanced provides advanced DDoS protection for AWS workloads, including EC2, CloudFront, and RDS. When integrated with CloudFront, Shield Advanced offers comprehensive detection and mitigation against large and sophisticated DDoS attacks, along with 24x7 access to the AWS DDoS Response Team (DRT). AWS WAF provides application-level protection, but for complete DDoS mitigation, Shield Advanced is the recommended solution.

Reference Extract from AWS Documentation / Study Guide:

" AWS Shield Advanced provides expanded DDoS attack protection for applications running on AWS. It offers always-on detection and automatic inline mitigations that minimize application downtime and latency. "

Source: AWS Certified Solutions Architect – Official Study Guide, Security and DDoS Protection section.

The requirements include:

Scalability: The solution must scale as video files are uploaded.

Long-running tasks: Processing tasks can take up to 30 minutes. AWS Lambda has a maximum execution time of 15 minutes, which rules out options that involve Lambda performing all the processing.

Serverless and event-driven architecture: Ensures cost-effectiveness and high availability.

Analysis of Options:

Option A:

AWS Lambda has a 15-minute timeout, which cannot support tasks that take up to 30 minutes.

EventBridge Scheduler is unnecessary for monitoring files when native event notifications are available.Not a valid choice.

Option B:

AWS Step Functions and AWS Fargate can handle long-running processes, but Amazon EFS is not the ideal storage for uploaded video files in a serverless architecture.

Processing tasks triggered by EFS events are not a common pattern and may introduce complexities.Not the best practice.

Option C:

Amazon S3 is used for storing uploaded files, which integrates natively with event-driven services like EventBridge and Step Functions.

Amazon S3 event notifications trigger a Step Functions workflow, which can orchestrate Fargate tasks to process large video files, meeting the scalability and execution time requirements.Correct choice.

Option D:

Similar to Option A, AWS Lambda cannot handle long-running processes due to its 15-minute timeout.

Invoking Lambda for processing directly is not feasible for tasks that take up to 30 minutes.Not a valid choice.

AWS References:

Amazon S3 Event Notifications:AWS Documentation - S3 Event Notifications

AWS Step Functions:AWS Documentation - Step Functions

AWS Fargate:AWS Documentation - Fargate

Comparison of Storage Services:AWS Storage Options

By leveragingAmazon S3,Step Functions, andFargate, this solution provides a scalable, efficient, and serverless approach to handling video processing tasks.

Amazon Macieis purpose-built for detecting PII in S3.

Option Auses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.

Options B and Dinvolve GuardDuty, which is not designed for PII detection.

Option Cuses SQS, which is less suitable for immediate notifications.

AmazonTextractcan extract text from the PDFs, andAmazon Comprehendis the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in anAmazon S3bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

For globally distributed consumers of REST APIs, API Gateway edge-optimized endpoints “route requests to the nearest CloudFront edge location, which then forwards them to your API in the [home] Region,” reducing latency for users worldwide and scaling automatically for unknown or spiky traffic. By contrast, Regional endpoints are intended for clients within the same or nearby Region and do not provide global edge acceleration. AWS Lambda provides automatic scaling for serverless backends; latency can be further reduced by right-sizing memory (which also increases CPU) and, when needed, enabling provisioned concurrency to keep functions initialized and eliminate cold starts for critical paths. Using NLBs across Regions is not serverless and adds operational complexity without CloudFront edge acceleration. Therefore, combining API Gateway edge-optimized REST APIs with Lambda meets the requirements of minimal global latency and unknown initial traffic.

Application Load Balancer (ALB) supports HTTP/HTTPS layer 7 routing, including header-based routing, path-based routing, and host-based routing.

AWS WAF is designed to provide rules-based filtering (block, allow, count) of HTTP(S) requests to protect applications from common exploits and malicious traffic.

ALB integrates directly with AWS WAF, so you can attach a web ACL with custom rules defined by the security team to block specific patterns while still using header-based routing.

Why the others are not correct:

A: Mutual TLS adds client certificate authentication but does not provide rules-based blocking or WAF-style inspection.

C: AWS Config is for configuration compliance and auditing, not request filtering.

D: NLB operates at layer 4 (TCP/UDP); it does not support HTTP header-based routing.

Using Service Control Policies (SCPs) to enforce permissions boundaries ensures that all created roles are constrained, regardless of attached policies. This is the most secure, scalable, and AWS-recommended preventive control.

Option A is the best answer because it introduces both decoupling and horizontal scalability with minimal infrastructure management. API Gateway provides a managed front door for the microservices, Lambda provides serverless compute that scales with request volume, and SQS provides durable buffering between transactional stages such as order creation and payment processing. AWS recommends queue-based decoupling when producers and consumers operate at different speeds or when spikes must be absorbed without losing work. The EKS and EC2 alternatives add significantly more operational burden, and caching does not solve the core workflow-scaling issue. Therefore, a serverless microservices architecture using API Gateway, Lambda, and SQS is the best fit.

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AWS Secrets Manager is purpose-built to securely store, manage, and rotate sensitive credentials such as database user names and passwords. Option A is the most operationally efficient solution because it eliminates manual password rotation, reduces human error, and centralizes secret lifecycle management. Secrets Manager integrates natively with Amazon Aurora, enabling automated credential rotation using AWS-managed or custom Lambda rotation logic. Once rotation is enabled, Secrets Manager updates the database credentials and stores the new values securely without requiring administrators to manually update files on EC2 instances.

By assigning IAM permissions to the secret, access can be tightly controlled using least-privilege principles. The application retrieves credentials at runtime, removing the need to store passwords locally on disk, which significantly improves security posture. Secrets Manager also provides auditing capabilities through AWS CloudTrail, allowing visibility into secret access and changes.

Option B (Systems Manager Parameter Store) can securely store secrets, but automated rotation is not natively supported in the same way as Secrets Manager. Implementing rotation with Parameter Store would require additional custom automation, increasing operational complexity. Option C stores credentials in S3, which is not designed for frequent credential rotation or secure secret access patterns, even when encrypted. Option D only encrypts credentials at rest on individual instances and does not address rotation, distribution, or centralized management, resulting in high operational overhead.

Therefore, A best meets the requirements by providing secure storage, automated monthly rotation, fine-grained access control, and minimal operational effort, aligning with AWS security and operational excellence best practices.

Amazon S3 Intelligent-Tiering is designed for unknown or unpredictable access patterns, automatically moving objects to the most cost-effective tier without performance impact.

It is ideal when you do not know object size or access frequency.

S3 Standard (Option B) works but costs more.

S3 One Zone-IA and Glacier Deep Archive (Options D and A) are not appropriate because data is frequently accessed for processing and persists only 24 hours.

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Creating a manual copy of the automated snapshot is the most operationally efficient option because it directly meets the requirement—retain a specific snapshot beyond the automated retention window—with the least added process and tooling. In Amazon RDS, automated backups and their snapshots are retained only for the configured backup retention period (up to the service maximum). When that retention period is exceeded, older automated snapshots are removed automatically. However, manual snapshots are retained until you explicitly delete them, so converting (copying) an automated snapshot to a manual snapshot is the standard operational approach to keep a point-in-time backup for long-term retention.

Option C is incorrect because you cannot extend automated snapshot retention beyond the maximum supported retention; also, setting “45 days” may still be within or beyond the service limits depending on the engine and configuration, and it doesn’t guarantee indefinite retention. Option B (export to S3) is not the most operationally efficient for the stated goal: exporting is a different workflow (often used for analytics, archiving in open formats, or cross-tool usage) and introduces extra steps, format considerations, and ongoing management in S3. Option D is also heavier operationally: native SQL Server backups require managing backup jobs, storage layout, restores, and permissions, and it shifts responsibility to the customer for operational correctness.

Therefore, A is the simplest and most AWS-native way to preserve an RDS snapshot long-term with minimal operational overhead.

AWS Backup offers centralized management, scheduling, and retention of backups for supported AWS services including Amazon DynamoDB. It enables compliance retention with minimal management.

From AWS Documentation:

“AWS Backup provides centralized backup management across AWS services, including DynamoDB. You can define backup plans, schedules, and retention policies to meet business or regulatory requirements.”

(Source: AWS Backup Developer Guide – Backing up DynamoDB Tables)

Why B is correct:

Automatically manages backup scheduling and retention (7 years).

Centralized compliance monitoring via AWS Backup Audit Manager.

Fully managed and requires no custom automation.

Why others are incorrect:

A: PITR is for continuous restore within 35 days, not long-term compliance retention.

C & D: Manual or Lambda-based backups require custom management and increase operational complexity.