SOA-C03 AWS Certified CloudOps Engineer - Associate Questions and Answers

According to the AWS Cloud Operations, Governance, and Compliance documentation, AWS Config provides managed rules that automatically evaluate resource configurations for compliance. The “required-tags” managed rule allows CloudOps teams to specify mandatory tags (e.g., Environment, Owner, CostCenter) and automatically detect non-compliant resources such as DynamoDB tables.

Furthermore, AWS Config supports automatic remediation through AWS Systems Manager Automation runbooks, enabling correction actions (for example, adding missing tags) without manual intervention. This automation minimizes operational overhead and ensures continuous compliance across multiple accounts.

Using a custom Lambda function (Options A or B) introduces unnecessary management complexity, while EventBridge rules alone (Option D) do not provide resource compliance tracking or historical visibility.

Therefore, Option C provides the most efficient, fully managed, and compliant CloudOps solution.

AWS CloudOps automation best practices recommend using AWS Systems Manager Quick Setup for organization-wide management and configuration of EC2 instances. The Default Host Management Configuration Quick Setup automatically enables Systems Manager capabilities such as Patch Manager, Inventory, Session Manager, and Automation across all managed instances within the organization.

When deployed from the management account, Quick Setup automatically integrates with AWS Organizations to propagate configuration and permissions to existing and future accounts. This meets the requirement for organization-wide management with no manual configuration or SSH access. AWS documentation notes:

“You can use Quick Setup in the management account of an organization in AWS Organizations to configure Systems Manager capabilities for all accounts and Regions. Quick Setup automatically keeps configurations up to date.”

Options B, C, and D require custom deployments or manual IAM updates, lacking centralized automation. Therefore, Option A fully satisfies CloudOps standards for automated provisioning and ongoing management of EC2 instances across an organization.

Per the AWS Cloud Operations and Service Catalog documentation, when a portfolio is shared across AWS accounts, the recipient account imports the shared portfolio.

The recipient CloudOps engineer cannot modify the original products or their configurations but can:

Add products from the imported portfolio into their local portfolios for deployment,

Control end-user access in the recipient account, and

Manage local constraints or permissions.

However, the recipient cannot edit, delete, or reconfigure the shared products (Options B, C, and D). The source (owner) account retains full administrative control over products, launch roles, and lifecycle policies.

This model aligns with AWS CloudOps principles of centralized governance with distributed self-service deployment across multiple accounts.

Thus, Option A is correct—imported portfolios allow the recipient to add products to a local portfolio but not alter the shared configuration.

According to the AWS Cloud Operations and ElastiCache documentation, cache evictions occur when the cache runs out of memory and must remove items to make space for new data.

To reduce evictions and retain frequently accessed items, AWS recommends increasing the total available memory — either by scaling up to larger node types or scaling out by adding shards/nodes. Migrating to a cluster with larger nodes is the simplest and most efficient solution because it immediately expands capacity without architectural changes.

Adjusting TTL (Options B and C) controls expiration timing, not memory allocation. Adding a single node (Option A) may help, but redistributing data requires resharding, introducing more complexity.

Thus, Option D provides the lowest operational overhead and ensures high cache hit rates by increasing total cache memory.

High availability requires removing single-AZ risk and eliminating manual recovery. The AWS Reliability best practices state to design for multi-AZ and automatic healing: Auto Scaling “helps maintain application availability and allows you to automatically add or remove EC2 instances” (AWS Auto Scaling User Guide). The Reliability Pillar recommends to “distribute workloads across multiple Availability Zones” and to “automate recovery from failure” (AWS Well-Architected Framework – Reliability Pillar). Attaching the Auto Scaling group to an ALB target group enables health-based replacement: instances failing load balancer health checks are replaced and traffic is routed only to healthy targets. Using an AMI in a launch template ensures consistent, repeatable instance configuration (AWS EC2 Launch Templates). Options A and C keep all instances in a single Availability Zone and rely on manual or ad-hoc restarts, which do not meet high-availability or resiliency goals. Option B only scales vertically and adds a restart rule; it neither removes the single-AZ failure domain nor provides automated replacement. Therefore, creating a multi-AZ EC2 Auto Scaling group with a launch template and attaching it to the ALB target group (Option D) is the CloudOps-aligned solution for resilience and business continuity.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The correct answer is D because Amazon S3 Express One Zone with directory buckets and zonal endpoints is specifically designed for single–Availability Zone, high-performance workloads such as HPC, machine learning, and analytics applications running on Amazon EC2. AWS CloudOps documentation states that S3 Express One Zone delivers single-digit millisecond latency and up to 10x higher request performance compared to general purpose S3 buckets when data is accessed from the same Availability Zone.

An S3 directory bucket is required to use the S3 Express One Zone storage class. These buckets are explicitly associated with a single Availability Zone and use zonal endpoints, which eliminate cross-AZ network hops and significantly reduce latency. Importing the data from the existing general purpose bucket ensures compatibility while achieving maximum throughput and lowest latency.

Option A is incorrect because S3 Transfer Acceleration is optimized for long-distance, internet-based transfers, not for in-Region HPC workloads. Option B is incorrect because lifecycle policies cannot move objects into S3 Express One Zone, and S3 Express One Zone does not use Regional endpoints. Option C is incorrect because general purpose buckets do not support zonal endpoints and therefore cannot achieve the same performance benefits.

AWS CloudOps performance optimization guidance clearly identifies S3 directory buckets with S3 Express One Zone and zonal endpoints as the optimal architecture for high-throughput, low-latency workloads in a single Availability Zone.

According to the AWS Cloud Operations and Identity Management documentation, when configuring federation between IAM Identity Center (formerly AWS SSO) and an external SAML 2.0 identity provider, two key prerequisites are required:

The IAM Identity Center SAML metadata file — This is uploaded to the external IdP to establish trust, define SAML endpoints, and enable identity federation.

The IdP metadata (including the public X.509 certificate) — This information is imported into IAM Identity Center to validate authentication assertions and encryption signatures.

IAM Identity Center and the IdP exchange this metadata to mutually establish secure, bidirectional federation.

Network-level details such as IP addresses (Option C) are unnecessary. Root access (Option D) or permissions to member accounts (Option E) are not required; only Control Tower or IAM administrative permissions in the management account are needed for setup.

Thus, the correct answer is A and B — the SAML metadata from both sides is required for federation.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:

With simple scaling policies, an Auto Scaling group performs one scaling activity when the alarm condition is met, then observes a default cooldown period (300 seconds) before another scaling activity of the same type can begin. CloudOps guidance explains that cooldown prevents rapid successive scale-outs by allowing time for the newly launched instance(s) to register with the load balancer and impact the metric. Even if the alarm breaches multiple times during the cooldown window, the group waits until the cooldown completes before evaluating and acting again. In this case, although RequestCountPerTarget exceeded the threshold twice within 180 seconds, the group will launch a single instance and then wait for cooldown before any additional scale-out can occur. Options A, C, and D do not reflect the behavior of simple scaling with cooldowns; A describes step/target-tracking-like behavior, and C/D are not Auto Scaling mechanics.

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For high availability and fast failover, ElastiCache for Redis supports replication groups with Multi-AZ and automatic failover. CloudOps guidance states that a primary node can be paired with one or more replicas across multiple Availability Zones; if the primary fails, Redis automatically promotes a replica to primary in seconds, thereby minimizing RTO. This architecture maintains in-memory data continuity without waiting for backup restore operations. Backups (Options B and D) provide durability but require restore and re-warm procedures that increase RTO and may impact application latency. Switching engines (Option A) to Memcached does not provide Redis replication/failover semantics and would not inherently improve resilience for this use case. Therefore, creating a read replica in a different AZ and enabling Multi-AZ with automatic failover is the prescribed CloudOps pattern to increase resilience and achieve the lowest practical RTO for Redis caches.

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The correct answer is B because cross-account role assumption requires two explicit permissions. AWS CloudOps documentation states that the target role must trust the principal, and the principal must be allowed to call sts:AssumeRole.

In the member account, the role’s trust policy must list the IAM group ARN (or the identity account) as a trusted principal. In the identity account, the IAM group must have an inline or attached policy that allows the sts:AssumeRole action for the target role ARN. This dual configuration enables secure and controlled cross-account access.

Option A is incorrect because trust policies cannot be attached to IAM groups. Option C is incorrect because sts:PassRole is used for passing roles to AWS services, not for assuming roles. Option D is incorrect because roles do not grant permissions via inline policies to principals.

This approach aligns precisely with AWS CloudOps guidance for multi-account IAM design.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The correct answer is A because AWS KMS symmetric customer managed keys with automatic key rotation provide encryption, access control, and compliance with minimal operational effort. AWS CloudOps documentation states that Amazon EBS encryption supports AWS KMS symmetric keys only, and customer managed keys allow administrators to define custom key policies to control access.

Automatic key rotation is supported for symmetric customer managed keys and rotates the backing key material once every year, fully satisfying the company’s rotation requirement without manual intervention. This approach minimizes operational overhead while maintaining strong security controls and auditability.

Option B is incorrect because AWS owned keys do not allow custom key policies and therefore cannot meet the access control requirement. Option C is incorrect because asymmetric KMS keys are not supported for EBS encryption. Option D is incorrect because imported key material requires manual rotation and re-import, increasing operational complexity and risk.

AWS CloudOps security best practices strongly recommend customer managed symmetric keys with automatic rotation when organizations need fine-grained access control, regulatory compliance, and low maintenance overhead.

To analyze and visualize custom statistics such as the p90 latency (90th percentile), a CloudWatch metric must be generated from the log data. The correct method is to create a metric filter that extracts the latency value from each log event and publishes it as a CloudWatch metric. Once the metric is published, percentile statistics (p90, p95, etc.) can be displayed in CloudWatch dashboards or alarms.

AWS documentation states:

“You can use metric filters to extract numerical fields from log events and publish them as metrics in CloudWatch. CloudWatch supports percentile statistics such as p90 and p95 for these metrics.”

Contributor Insights (Option A) is for analyzing frequent contributors, not numeric distributions. Subscription filters (Option C) are used for log streaming, and Application Insights (Option D) provides monitoring of application health but not custom p90 statistics. Hence, Option B is the CloudOps-aligned, minimal-overhead solution for percentile latency monitoring.

In this scenario, the EC2 instances pass their EC2 status checks, indicating that the operating system is responsive. However, the application hosted on the instance is failing intermittently, returning HTTP 500 errors. This demonstrates a discrepancy between the instance-level health and the application-level health.

According to AWS CloudOps best practices under Monitoring, Logging, Analysis, Remediation and Performance Optimization (SOA-C03 Domain 1), Auto Scaling groups should incorporate Elastic Load Balancing (ELB) health checks instead of relying solely on EC2 status checks. The ELB health check probes the application endpoint (for example, HTTP or HTTPS target group health checks), ensuring that the application itself is functioning correctly.

When an instance fails an ELB health check, Amazon EC2 Auto Scaling will automatically mark the instance as unhealthy and replace it with a new one, ensuring continuous availability and performance optimization.

Extract from AWS CloudOps (SOA-C03) Study Guide – Domain 1:

“Implement monitoring and health checks using ALB and EC2 Auto Scaling integration. Application Load Balancer health checks allow Auto Scaling to terminate and replace instances that fail application-level health checks, ensuring consistent application performance.”

Extract from AWS Auto Scaling Documentation:

“When you enable the ELB health check type for your Auto Scaling group, Amazon EC2 Auto Scaling considers both EC2 status checks and Elastic Load Balancing health checks to determine instance health. If an instance fails the ELB health check, it is automatically replaced.”

Therefore, the correct answer is B, as it ensures proper application-level monitoring and remediation using ALB-integrated ELB health checks—a core CloudOps operational practice for proactive incident response and availability assurance.

References (AWS CloudOps Verified Source Extracts):

AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide: Domain 1 – Monitoring, Logging, and Remediation.

AWS Auto Scaling User Guide: Health checks for Auto Scaling instances (Elastic Load Balancing integration).

AWS Well-Architected Framework – Operational Excellence and Reliability Pillars.

AWS Elastic Load Balancing Developer Guide – Target group health checks and monitoring.

The AWS Cloud Operations and Compute documentation explains that placement groups control how EC2 instances are physically arranged within AWS data centers to optimize network performance.

Among the available placement strategies:

Cluster placement groups place instances physically close together within a single Availability Zone, connected through high-bandwidth, low-latency networking (ideal for tightly coupled, HPC, or distributed workloads).

Spread placement groups distribute instances across distinct racks or Availability Zones for fault tolerance, increasing latency.

Partition placement groups separate instances into partitions for isolation, not latency reduction.

Therefore, to minimize latency for workloads such as computational clusters, the CloudOps engineer should use a cluster placement group. This placement ensures single-digit microsecond latency and enhanced packet rate performance between instances.

Elastic IPs (Option B) do not influence internal networking. Jumbo frames (Option C) can marginally improve throughput but do not reduce propagation latency. Spread placement (Option D) increases distance, worsening latency.

Hence, Option A — using a cluster placement group — delivers the lowest possible network latency and is AWS’s best-practice design for HPC-style clusters.

The AWS Cloud Operations and Governance documentation specifies that AWS Config can be paired with AWS Systems Manager Automation runbooks for automatic remediation of noncompliant resources.

For SSH restrictions, the restricted-ssh managed rule detects any security group allowing inbound traffic on port 22. To automatically remediate these findings, AWS provides the AWS-DisableIncomingSSHOnPort22 runbook. This runbook programmatically removes inbound rules that allow port 22 traffic from affected security groups.

This approach achieves continuous compliance with minimal human intervention. By contrast, sending notifications (Option A) does not enforce remediation, API-based scripts (Option C) add operational overhead, and manual remediation (Option D) violates automation best practices.

Therefore, the most efficient CloudOps solution is Option B, using AWS Config with the AWS-DisableIncomingSSHOnPort22 automation runbook for automatic, scalable enforcement.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

Amazon Machine Images (AMIs) are Region-specific resources. AWS CloudOps documentation explicitly states that an AMI created in one Region cannot be used to launch instances in another Region unless it is copied to the target Region. Therefore, the SysOps administrator must copy the AMI to both us-east-1 and us-east-2.

The AMI copy process creates a new AMI in each destination Region and automatically copies the underlying snapshots. Once the AMIs exist in the target Regions, they can be referenced in launch templates, Auto Scaling groups, or AWS CloudFormation templates for consistent multi-Region deployments.

Option B is incorrect because making an AMI public does not replicate it across Regions. Option C is incorrect because sharing an AMI only grants account-level access within the same Region. Option D is incorrect because AMIs cannot be launched from Amazon S3 directly.

This approach aligns with AWS CloudOps automation practices for multi-Region application deployment and disaster recovery readiness.

The AWS Cloud Operations and Monitoring documentation specifies that the Amazon CloudWatch Agent is the recommended tool for collecting system and application logs from EC2 instances. The agent pushes these logs into a centralized CloudWatch Logs group, providing durable storage and real-time monitoring.

Once the logs are centralized, a CloudWatch Metric Filter can be configured to search for specific error keywords (for example, “ERROR” or “FAILURE”). This filter transforms matching log entries into custom metrics. From there, a CloudWatch Alarm can monitor the metric threshold and publish notifications to an Amazon SNS topic, which can send email or SMS alerts to subscribed recipients.

This combination provides a fully automated, managed, and serverless solution for log aggregation and error alerting. It eliminates the need for manual cron jobs (Option B), custom scripts (Option D), or Lambda-based log streaming (Option C).

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The requirement is classic active-passive (production in us-east-1, DR in us-west-2 “only for failover”). The most operationally efficient and purpose-built solution is Route 53 failover routing combined with health checks. With failover routing, Route 53 designates one record as PRIMARY (us-east-1) and another as SECONDARY (us-west-2). Route 53 continuously evaluates the health check associated with the primary endpoint (commonly the ALB DNS name or a specific health-check path). If the primary fails, Route 53 automatically returns the secondary record, directing client DNS resolution to the DR region. This ensures us-west-2 is used only when us-east-1 is unhealthy, directly matching the requirement.

Latency routing (Option B) is designed to route users to the region with the lowest latency, which can actively send traffic to us-west-2 even when us-east-1 is healthy—violating the “DR only” constraint. Options C and D introduce custom automation (CloudWatch + Lambda + DNS record updates) that increases operational overhead, adds failure modes, and is unnecessary because Route 53 already provides managed health-check-based failover. Additionally, “EC2 instance terminated” is not a reliable proxy for full application availability, and DNS modification automation is more complex than using native Route 53 failover policies.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

To ensure video files are cached at CloudFront edge locations after migrating the files to Amazon S3, CloudFront must be able to fetch those video objects directly from S3 as an origin. The most operationally straightforward pattern is to add S3 as a second origin and create a separate cache behavior that routes requests for video paths (for example, /video/* or /*.mp4) to the S3 origin. With this configuration, CloudFront caches the S3-served objects at edge locations according to the cache policy/headers, while the existing ALB origin continues serving dynamic application paths. This isolates static media delivery from the application tier and improves performance by maximizing cache hits at the edge.

Option A is not how CloudFront origin selection works: CloudFront does not “redirect” to S3 based on headers; it selects an origin per behavior. Option C (signed URLs) is an access-control mechanism and does not, by itself, ensure the objects are retrieved from S3 or cached correctly. Option D (origin groups) is for origin failover (primary/secondary) and does not provide path-based routing to ensure videos come from S3.

The AWS Cloud Operations and Governance documentation defines that enabling Organizational View in AWS Health allows the management account in AWS Organizations to view and aggregate health events from all member accounts.

This feature provides a single-pane-of-glass view of service health issues, account-specific events, and planned maintenance across the organization — without requiring additional automation or data pipelines.

Alternative options (B, C, and D) require custom integration and ongoing maintenance. CloudTrail does not natively forward AWS Health events, and custom Lambda or DynamoDB approaches increase complexity.

Therefore, Option A — enabling the Organizational View feature in AWS Health — is the most operationally efficient and AWS-recommended solution.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

To meet the requirement to log and audit any principal that publishes to SNS topics and interacts with SQS queues, the correct service is AWS CloudTrail, because CloudTrail records API activity (who did what, when, and from where). Enabling data events (where supported/required for deeper visibility) provides detailed records for operations such as publishing messages and sending/receiving messages. Delivering CloudTrail logs to Amazon S3 provides durable retention and supports querying workflows.

To ensure that communication uses VPC endpoints, the company should configure VPC endpoints for SNS and SQS and then validate usage by inspecting CloudTrail event records. CloudTrail includes endpoint-related context fields (for example, a VPC endpoint identifier) that allow auditors to confirm that the request path used a VPC endpoint rather than traversing the public internet. This directly addresses the “must use VPC endpoints” control with auditable evidence.

The other options do not satisfy both requirements. CloudWatch Logs does not automatically capture SNS/SQS API caller identity for publish/send/receive operations in the same authoritative way CloudTrail does. EventBridge can capture service events but is not the primary audit log of API calls and does not inherently prove VPC endpoint usage per request. Inspecting a VPC endpoint field in CloudWatch Logs is not the standard audit mechanism for these API calls.

Per the AWS Cloud Operations and S3 Data Management documentation, Cross-Region Replication (CRR) automatically replicates new objects between S3 buckets across Regions. However, CRR alone does not retroactively replicate existing objects created before replication configuration. To include such objects, AWS introduced S3 Batch Replication.

S3 Batch Replication scans the source bucket and replicates all existing objects that were not copied previously. Additionally, it can retry failed replication tasks automatically, ensuring regulatory compliance for complete dataset replication.

S3 Replication Time Control (S3 RTC) guarantees predictable replication times for new objects only—it does not cover previously stored data. S3 Lifecycle rules (Option D) move or transition objects between storage classes or buckets, but not in a replication context.

Therefore, the correct solution is to use S3 Cross-Region Replication (CRR) combined with S3 Batch Replication to ensure all current and future data is synchronized across Regions with retry capability.

AWS Systems Manager Session Manager allows secure, auditable instance access without SSH keys or inbound ports. To control access based on instance tags, CloudOps best practices require two configurations:

Attach an IAM policy to users or groups granting ssm:StartSession, ssm:DescribeInstanceInformation, and ssm:DescribeSessions.

Include a Condition element in the IAM policy referencing instance tags, such as Condition: {"StringEquals": {"ssm:resourceTag/Environment": "Production"}}.

This ensures users can start sessions only with instances that have matching tags, providing fine-grained access control.

AWS CloudOps documentation under Security and Compliance states:

“Use IAM policies with resource tags in the Condition element to restrict which managed instances users can access using Session Manager.”

Options B and D incorrectly suggest attaching roles or service accounts that are not relevant to user-level access control. Option C (placement groups) pertains to networking and performance, not access management. Therefore, A and E together provide tag-based, least-privilege access as required.

Tagging is essential for governance, cost management, and automation in CloudOps operations. The AWS Organizations tag policies feature allows centralized definition and enforcement of required tag keys and accepted values across all accounts in an organization. According to the AWS CloudOps study guide under Deployment, Provisioning, and Automation, tag policies enable automatic validation of tags, ensuring consistency with minimal manual overhead.

Once tagging consistency is enforced, enabling cost allocation tags in the AWS Billing and Cost Management console allows accurate cost distribution per business unit. AWS documentation states:

“Use AWS Organizations tag policies to standardize tags across accounts. You can activate cost allocation tags in the Billing console to track and allocate costs.”

Option B introduces unnecessary complexity with Lambda automation. Option C detects but does not enforce tagging. Option D limits flexibility to Service Catalog resources only. Therefore, Option A provides a centrally managed, automated, and low-overhead solution that meets CloudOps tagging and cost-tracking requirements.