Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

A “No space left on device” error typically results from data skew or large shuffle stages. The best actions are:

B. Monitor using Spark UI and Glue metrics to find skewed partitions or executor issues.

D. Use --write-shuffle-files-to-s3 to offload intermediate data to S3 instead of local disk, and apply salting to reduce skew.

“You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.

AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. References:

AWS Glue

Working with Python Shell Jobs

pandas

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handles idempotency. By embedding a unique ID in each record, the application can identify and remove duplicate records during processing.

Exactly-Once Processing:

Kinesis Data Streams does not natively support exactly-once processing. Therefore, idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.

This pattern is widely used for achieving exactly-once semantics in distributed systems.

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena’s federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena’s support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

 AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:

AWS Glue

[AWS Glue Data Catalog]

[AWS Glue Developer Guide]

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

In Amazon Athena, you can use CREATE TABLE AS SELECT with WITH NO DATA to create an empty copy of an existing table’s schema:

“The query CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA; creates a new table with the same schema but without copying over the data.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the most efficient way to create an empty version of the existing table.

 The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.

AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entire table metadata. This reduces the query planning time and improves the query performance1.

Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to ‘project’, or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.

Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.

Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.

Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:

Improve query performance using AWS Glue partition indexes

Partition projection with Amazon Athena

Bucketing vs Partitioning

Columnar Storage Formats

S3DistCp

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated. References:

Amazon EC2 Instance Store

Amazon EBS Volumes

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

Amazon RDS is a fully managed service that provides relational databases in the cloud. Amazon RDS for MySQL is one of the supported database engines that you can use to run your applications. Amazon RDS provides various features and tools to monitor and optimize the performance of your DB instances, such as Performance Insights, Enhanced Monitoring, CloudWatch metrics and alarms, etc.

Using the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization and optimizing the problematic queries will help reduce the CPU utilization of the DB instance. Performance Insights is a feature that allows you to analyze the load on your DB instance and determine what is causing performance issues. Performance Insights collects, analyzes, and displays database performance data using an interactive dashboard. You can use Performance Insights to identify the top SQL statements, hosts, users, or processes that are consuming the most CPU resources. You can also drill down into the details of each query and see the execution plan, wait events, locks, etc. By using Performance Insights, you can pinpoint the root cause of the high CPU utilization and optimize the queries accordingly. For example, you can rewrite the queries to make them more efficient, add or remove indexes, use prepared statements, etc.

Implementing caching to reduce the database query load will also help reduce the CPU utilization of the DB instance. Caching is a technique that allows you to store frequently accessed data in a fast and scalable storage layer, such as Amazon ElastiCache. By using caching, you can reduce the number of requests that hit your database, which in turn reduces the CPU load on your DB instance. Caching also improves the performance and availability of your application, as it reduces the latency and increases the throughput of your data access. You can use caching for various scenarios, such as storing session data, user preferences, application configuration, etc. You can also use caching for read-heavy workloads, such as displaying product details, recommendations, reviews, etc.

The other options are not as effective as using Performance Insights and caching. Modifying the database schema to include additional tables and indexes may or may not improve the CPU utilization, depending on the nature of the workload and the queries. Adding more tables and indexes may increase the complexity and overhead of the database, which may negatively affect the performance. Rebooting the RDS DB instance once each week will not reduce the CPU utilization, as it will not address the underlying cause of the high CPU load. Rebooting may also cause downtime and disruption to your application. Upgrading to a larger instance size may reduce the CPU utilization, but it will also increase the cost and complexity of your solution. Upgrading may also not be necessary if you can optimize the queries and reduce the database load by using caching. References:

Amazon RDS

Performance Insights

Amazon ElastiCache

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 3: Data Storage and Management, Section 3.1: Amazon RDS

To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.

SCPs (A & C) set permissions boundaries, but do not grant access.

Option D incorrectly refers to the Lambda function – but the Kinesis resource must allow access.

“You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream.”

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

Amazon S3 Lifecycle Policies

S3 Storage Classes

Cost Management and Data Optimization Using Lifecycle Policies

AWS Data Engineering Documentation

 Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:

Amazon AppFlow

Amazon AppFlow | SaaS Integrations List

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.

The other options are not optimal for the following reasons:

A. Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

E. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.

1: Connecting to an Amazon RDS DB instance

2: Configuring a Lambda function to access resources in a VPC

3: Working with security groups

Network ACLs

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift

 Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:

Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)

Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)

Monitoring with AWS Glue job run insights (Section: Requirements)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)

Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.

Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.

Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.

Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Introducing Amazon S3 Object Lambda – Use Your Code to Process Data as It Is Being Retrieved from S3

Using Bucket Policies and User Policies - Amazon Simple Storage Service

AWS Glue Documentation

What is Amazon API Gateway? - Amazon API Gateway

To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:

Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.

Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.

Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.

Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.

Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.

Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.

Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.

1: Amazon S3 storage classes - Amazon Simple Storage Service

2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service

3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service

[4]: Expiring objects - Amazon Simple Storage Service

[5]: Managing your storage lifecycle - Amazon Simple Storage Service

[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service

[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What’s New with AWS

The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provide object-level tracking for S3 to capture detailed API actions on specific S3 objects, including the user who performed the action.

AWS CloudTrail:

CloudTrail can monitor API calls made to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.

CloudTrail logs include details such as the user identity, which is essential for meeting the requirement of including the username in notifications.

The CloudTrail logs can be forwarded to Amazon CloudWatch to trigger alarms based on certain access patterns (e.g., violations of specific policies).

AWS Database Migration Service (AWS DMS) is purpose-built to migrate and continuously replicate data from both AWS-hosted and on-premises databases. It supports full-load (existing data) and change data capture (CDC) for ongoing changes, making it the most cost-effective and operationally simple solution in this scenario.

“DMS supports both full-load and continuous replication via CDC. This enables replicating existing and future data from various sources to a data lake in Amazon S3.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

AWS Glue is not suitable for real-time CDC replication across hybrid environments.

To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.

Option A: DELETE FROM materialized_view_name WHERE 1=1;This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.

Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don’t remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).

The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:

Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)

When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.

A. Confirm that Athena is pointing to the correct Amazon S3 location:

One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.

 This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:

Amazon Redshift streaming ingestion

Creating an external schema for Amazon Kinesis Data Streams

Creating a materialized view for Amazon Kinesis Data Streams

The correct solution is C: AWS Secrets Manager.

Why? AWS Secrets Manager is a fully managed service that helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure.

It allows for automatic rotation of secrets without requiring changes to the application code, meeting the requirement of minimal operational overhead.

Applications can securely retrieve credentials using Secrets Manager APIs, and the service integrates with AWS Identity and Access Management (IAM) to control access to secrets.

“You can use Secrets Manager to store credentials and to configure automatic rotation.”

AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.

Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.

The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:

AWS Step Functions

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

The company wants to gather information about incomplete multipart uploads and outdated versions in its Amazon S3 buckets to optimize storage costs.

Option B: Use Amazon S3 Inventory configurations reports to gather the information.S3 Inventory provides reports that can list incomplete multipart uploads and versions of objects stored in S3. It offers an easy, automated way to track object metadata across buckets, including data necessary for cost optimization, without manual effort.

Options A (AWS CLI), C (S3 Storage Lens), and D (usage reports) either do not specifically gather the required information about incomplete uploads and outdated versions or require more manual intervention.

AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3. You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.

“Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort. Automate validation by triggering jobs through EventBridge.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.

Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.

Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.

The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.

Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.

Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.

Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.

Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.

AWS Step Functions allows you to orchestrate ETL jobs and workflows by integrating with AWS Glue and EventBridge. It supports both scheduled and event-driven executions with minimal operational overhead, making it ideal for hybrid triggering.

AWS Glue workflows are limited in orchestration logic.

MWAA (option B) is powerful but requires more infrastructure management.

EMR Serverless + Lambda (option D) is possible but involves more components.

“Use Step Functions to coordinate multiple services like Glue and EventBridge for event-driven or scheduled workflows.”

 An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.

The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References:

What is a data lake?

Choosing a data format for your data lake

Using the MERGE INTO command in Delta Lake

[AWS Lambda quotas]

[AWS Database Migration Service quotas]

For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.

When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.

This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.

“For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance.”

– Ace the AWS Certified Data Engineer – Associate Certification – version 2 – apple.pdf

“Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC.”

– Ace the AWS Certified Data Engineer – Associate Certification – version 2 – apple.pdf

Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.

The telecommunications company needs a low-latency solution to detect sudden drops in network usage from real-time data collected throughout the day.

Option B: Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (Amazon Kinesis Data Analytics) application to detect drops in network usage.Using Amazon Kinesis with Managed Service for Apache Flink (formerly Kinesis Data Analytics) is ideal for real-time stream processing with minimal latency. Flink can analyze the incoming data stream in real-time and detect anomalies, such as sudden drops in usage, which makes it the best fit for this scenario.

Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases) or do not provide the same real-time, low-latency processing that is critical for this use case.

Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user’s PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:

Amazon Athena

AWS Lake Formation

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

The goal is to archive historical data from an Amazon Redshift data warehouse while combining live transactional data from Amazon Aurora PostgreSQL with current and historical data in a cost-efficient manner. The company wants to keep only the last 15 months of data in Redshift to reduce costs.

Option A: "Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database."Redshift Federated Query allows querying live transactional data directly from Aurora PostgreSQL without having to move it into Redshift, thereby enabling seamless integration of the current data in Redshift and live data in PostgreSQL. This is a cost-effective approach, as it avoids unnecessary data duplication.

Option C: "Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3."This option uses Amazon Redshift Spectrum, which enables Redshift to query data directly in S3 without moving it into Redshift. By unloading older data (older than 15 months) to S3, and then using Spectrum to access it, this approach reduces storage costs significantly while still allowing the data to be queried when necessary.

Option B (Redshift Spectrum for live PostgreSQL data) is not applicable, as Redshift Spectrum is intended for querying data in Amazon S3, not live transactional data in Aurora.

Option D (S3 Glacier Flexible Retrieval) is not suitable because Glacier is designed for long-term archival storage with infrequent access, and querying data in Glacier for analytics purposes would incur higher retrieval times and costs.

Option E (materialized views) would not meet the need to archive data or combine it from multiple sources; it is best suited for combining frequently accessed data already in Redshift.

 AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.

AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.

AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.

Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:

AWS Glue Workflows

AWS Step Functions

AWS Glue Studio

Amazon MWAA

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioning is the best solution.

S3 Versioning:

S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you can recover from accidental deletions or modifications.

Enabling versioning requires minimal overhead, as it is a bucket-level setting and does not require additional backup processes or data replication.

Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.

 The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.

Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.

Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can be used for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.

Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:

Amazon S3 - Cloud Object Storage

EMR File System (EMRFS)

AWS Graviton2 Processor-Powered Amazon EC2 Instances

[Plan and Configure EC2 Instances]

[Amazon EC2 Spot Instances]

[Best Practices for Amazon EMR]

 The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC’s route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:

Troubleshoot the AWS Glue error “VPC S3 endpoint validation failed”

Amazon VPC endpoints for Amazon S3

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

In AWS Glue’s SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.

The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.

“A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Step 1: Log Collection (FluentBit and CloudWatch)

Option A suggests using FluentBit to collect logs and OpenTelemetry to collect traces.

FluentBit is a lightweight log processor that integrates with Amazon EKS to collect and forward logs from Kubernetes clusters. It is widely used with minimal overhead, making it an ideal choice for log collection in this scenario. FluentBit is also natively compatible with AWS services.

OpenTelemetry is a popular framework to collect traces from distributed applications. It provides observability, making it easier to monitor microservices.

This combination allows you to effectively gather both logs and traces with minimal setup and configuration, aligning with the goal of least development effort.

CloudWatch can be used to monitor logs (Option B and C). However, for applications that need more custom and fine-grained control over logging mechanisms, FluentBit and OpenTelemetry are the preferred choice in microservice environments.

Step 2: Log and Trace Correlation (Amazon OpenSearch)

Option D (Amazon OpenSearch) is specifically designed to search, analyze, and visualize logs, metrics, and traces in real-time. OpenSearch allows you to correlate logs and traces effectively.

With Amazon OpenSearch, you can set up dashboards that help in visualizing both logs and traces together, which assists in identifying any failure points across the entire request flow.

It offers integrations with FluentBit and OpenTelemetry, ensuring that both logs from the EKS cluster and application traces are centrally collected, stored, and correlated without additional heavy development.

Step 3: Why Other Options Are Not Suitable

Option B (Amazon Kinesis) is designed for real-time data streaming and analytics but is not as well-suited for tracing microservice requests and logs correlation compared to OpenSearch.

Option C (Amazon MSK) provides a managed Kafka streaming service, but this adds complexity when trying to integrate and correlate logs and traces from a microservice environment. Setting up Kafka requires more development effort compared to using FluentBit and OpenTelemetry.

Option E (AWS Glue) is primarily an ETL (Extract, Transform, Load) service. While Glue is powerful for data processing, it is not a native tool for log and trace correlation, and using it would add unnecessary complexity for this use case.

Conclusion:

To meet the requirements with the least development effort:

Use FluentBit for log collection and OpenTelemetry for tracing (Option A).

Correlate logs and traces using Amazon OpenSearch (Option D).

This approach leverages AWS-native services designed for seamless integration with microservices hosted on Amazon EKS and ensures effective monitoring with minimal overhead.

The best practice to restrict Amazon S3 access to specific VPCs is to use a bucket policy with a StringEquals or StringLike condition on aws:SourceVpc. This ensures only requests from a specified VPC are allowed.

IAM policies (option C) control who can access the resource but are not suitable alone to restrict by VPC.

Security Groups and NACLs (options B and D) do not apply to Amazon S3 because it is a global service and not VPC-bound.

“You can restrict access to your S3 bucket so that only requests coming from a specific VPC endpoint are allowed.”

Source: AWS Documentation – Amazon S3 Bucket Policies for VPC Endpoints

To identify the reason why the Step Functions state machine is not able to run the EMR jobs, the company should take the following steps:

Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. The state machine code should have an IAM role that allows it to invoke the EMR APIs, such as RunJobFlow, AddJobFlowSteps, and DescribeStep. The state machine code should also have IAM permissions to access the Amazon S3 buckets that the EMR jobs use as input and output locations. The company can use Access Analyzer for S3 to check the access policies and permissions of the S3 buckets12. Therefore, option B is correct.

Query the flow logs for the VPC. The flow logs can provide information about the network traffic to and from the EMR cluster that is launched in the VPC. The company can use the flow logs to determine whether the traffic that originates from the EMR cluster can successfully reach the data providers, such as Amazon RDS, Amazon Redshift, or other external sources. The company can also determine whether any security group that might be attached to the EMR cluster allows connections to the data source servers on the informed ports. The company can use Amazon VPC Flow Logs or Amazon CloudWatch Logs Insights to query the flow logs3 . Therefore, option D is correct.

Option A is incorrect because it suggests using AWS CloudFormation to automate the Step Functions state machine deployment. While this is a good practice to ensure consistency and repeatability of the deployment, it does not help to identify the reason why the state machine is not able to run the EMR jobs. Moreover, creating a step to pause the state machine during the EMR jobs that fail and wait for a human user to send approval through an email message is not a reliable way to troubleshoot the issue. The company should use the Step Functions console or API to monitor the execution history and status of the state machine, and use Amazon CloudWatch to view the logs and metrics of the EMR jobs .

Option C is incorrect because it suggests changing the AWS Step Functions state machine code to use Amazon EMR on EKS. Amazon EMR on EKS is a service that allows you to run EMR jobs on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. While this service has some benefits, such as lower cost and faster execution time, it does not support all the features and integrations that EMR on EC2 does, such as EMR Notebooks, EMR Studio, and EMRFS. Therefore, changing the state machine code to use EMR on EKS may not be compatible with the existing data pipeline and may introduce new issues.

Option E is incorrect because it suggests checking the retry scenarios that the company configured for the EMR jobs. While this is a good practice to handle transient failures and errors, it does not help to identify the root cause of why the state machine is not able to run the EMR jobs. Moreover, increasing the number of seconds in the interval between each EMR task may not improve the success rate of the jobs, and may increase the execution time and cost of the state machine. Configuring an Amazon SNS topic to store the error messages may help to notify the company of any failures, but it does not provide enough information to troubleshoot the issue.

1: Manage an Amazon EMR Job - AWS Step Functions

2: Access Analyzer for S3 - Amazon Simple Storage Service

3: Working with Amazon EMR and VPC Flow Logs - Amazon EMR

[4]: Analyzing VPC Flow Logs with Amazon CloudWatch Logs Insights - Amazon Virtual Private Cloud

[5]: Monitor AWS Step Functions - AWS Step Functions

[6]: Monitor Amazon EMR clusters - Amazon EMR

[7]: Amazon EMR on Amazon EKS - Amazon EMR

AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic.

AWS Glue Workflows:

AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.

In the event of incomplete data, an EventBridge event can be generated to notify via SNS.

Problem Analysis:

The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.

The goal is to implement quality checks with minimal implementation effort.

Key Considerations:

AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.

DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.

Solution Analysis:

Option A: SQL Transform

SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.

Option B: Evaluate Data Quality Transform + DQDL

AWS Glue’s built-in Evaluate Data Quality transform is designed for this use case.

Allows defining thresholds and rules in DQDL with minimal coding effort.

Option C: Custom Transform with PyDeequ

PyDeequ is a powerful library but adds unnecessary complexity compared to Glue’s native features.

Option D: Custom Transform with Great Expectations

Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.

Final Recommendation:

Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.

AWS Glue Data Quality

DQDL Syntax and Examples

AWS Glue Studio Documentation

Amazon Redshift supports dynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column’s values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).

“With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.

 A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.

For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. References:

1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS

2: AWS Glue - Developer Guide

3: AWS Lake Formation - Features

[4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue

[5]: Amazon S3 - Features

[6]: Amazon Athena - Features

Problem Analysis:

The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.

If a file is incomplete or empty, the previous day’s file should be retained.

Need a solution to validate files before overwriting the existing file.

Key Considerations:

Automate data validation with minimal human intervention.

Use built-in AWS Glue capabilities for ease of integration.

Ensure robust validation for missing or incomplete data.

Solution Analysis:

Option A: Lambda Function for Validation

Lambda can validate files, but it would require custom code.

Does not leverage AWS Glue’s built-in features, adding operational complexity.

Option B: AWS Glue Data Quality Rules

AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.

Rules can validate if required fields are missing or if the file is empty.

Automatically integrates into the existing ETL pipeline.

If validation fails, retain the previous day’s file.

Option C: AWS Glue Studio with Filling Missing Values

Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.

Does not handle empty files effectively.

Option D: Athena Query for Validation

Athena can drop rows with missing values, but this is a post-hoc solution.

Requires manual intervention to copy the corrected file to S3, increasing complexity.

Final Recommendation:

Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.

This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.

Implementation Steps:

Enable AWS Glue Data Quality in the existing ETL pipeline.

Define DQDL Rules, such as:

Check if a file is empty.

Verify required fields are present and non-null.

Configure the pipeline to proceed with overwriting only if the file passes validation.

In case of failure, retain the previous day’s file.

AWS Glue Data Quality Overview

Defining DQDL Rules

AWS Glue Studio Documentation

The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3

s3

is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs.

Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3.

Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3

, which is required for bookmarks to work as expected.

For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let’s break this down:

AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift.

AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real-time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing.

Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers.

Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings.

This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.

The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.

The other options are not optimal for the following reasons:

A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.

B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

1: Amazon Redshift VACUUM

2: Amazon Redshift Interleaved Sorting

3: Amazon Redshift ANALYZE

 This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References:

Choose the best distribution style

Distribution styles

Working with data distribution styles

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

“Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Options with “100” use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.

Problem Analysis:

The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.

Data updates must propagate quickly to OpenSearch for analytics or search purposes.

Key Considerations:

DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).

Integration with AWS Lambda allows seamless processing of these changes.

OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.

Solution Analysis:

Option A: Step Functions with Periodic Export

Not suitable for near-real-time updates; introduces significant latency.

Operationally complex to manage periodic exports and S3 data ingestion.

Option B: AWS Glue Job

AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.

Option C: DynamoDB Streams + Lambda

DynamoDB Streams capture changes in near real time.

Lambda can process these streams and use the OpenSearch API to update the index.

This approach provides low latency and seamless integration with minimal operational overhead.

Option D: Custom OpenSearch Plugin

Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.

Implementation Steps:

Enable DynamoDB Streams for the relevant DynamoDB tables.

Create a Lambda function to process stream records:

Parse insert, update, and delete events.

Use OpenSearch APIs to index or update documents based on the event type.

Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.

Monitor and log errors for debugging and operational health.

Amazon DynamoDB Streams Documentation

AWS Lambda and DynamoDB Integration

Amazon OpenSearch Service APIs