Free Practice Questions for Amazon Web Services Data-Engineer-Associate Exam

This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on-premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes. AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.

The other options are not optimal for the following reasons:

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.

C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.

D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers’ ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.

1: AWS Glue Data Catalog

2: AWS Glue Crawlers

Amazon Aurora

AWS Lambda

Amazon DynamoDB

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.”

Option A is correct because AWS Glue crawlers are the AWS-native service for discovering metadata and updating the AWS Glue Data Catalog. AWS documentation says that crawlers can crawl data stores, infer schema, and upon completion create or update tables in the Data Catalog. For schema-change tracking on an Amazon Redshift table, the crawler can connect through JDBC, inspect the table metadata, and update the Glue Data Catalog on a schedule. This directly satisfies the requirement to implement a cataloging solution that tracks schema changes over time.

Option B is incorrect because AWS DataSync is for data transfer, not metadata cataloging. Option C is also incorrect because AWS SCT is intended for schema conversion and migration assessments, not ongoing catalog synchronization into a Hive metastore for this use case. Option D is not the best answer because the requirement is for an AWS cataloging solution, and the standard managed catalog service here is the AWS Glue Data Catalog, not an external Apache Hive metastore. The daily scheduled crawler against Redshift with Glue Data Catalog updates is the most direct and exam-aligned solution for tracking schema changes.

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

AWS Step Functions natively emits state change events to Amazon EventBridge, which can trigger an Amazon SNS notification. This is the most direct and real-time way to alert on success/failure without relying on custom logging or polling.

“Step Functions automatically emits status changes that EventBridge can capture to trigger alerts or workflows. Use EventBridge to invoke an SNS topic for real-time alerts on job status.”

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

This provides real-time alerting and the least operational overhead.

Option A is correct because AWS Glue is a serverless ETL service built to run PySpark workloads at scale with minimal infrastructure management. AWS documentation states that you can install additional Python modules and libraries for use with AWS Glue ETL jobs, including by using the --additional-python-modules parameter and Amazon S3 paths for wheel artifacts or other supported package delivery methods. That directly addresses the requirement for custom internal libraries. Since the data is already in Amazon S3 and the result must be loaded into Amazon Redshift, Glue is a natural low-overhead fit for this daily transformation pipeline.

Option B and C require managing compute infrastructure or cluster lifecycle, which increases operational overhead. Option D is not the best fit because SageMaker Processing is designed primarily for ML-oriented data preparation, not as the standard AWS service for large-scale ETL into Redshift. The question explicitly asks for least operational overhead with scalable PySpark and custom libraries, and AWS Glue provides exactly that managed capability.

Option B is correct because the requirement is to retain a very large dataset indefinitely and analyze it from Amazon Redshift in the most cost-effective way. Amazon Redshift Spectrum allows Redshift to query data directly in Amazon S3 without loading all of the data into Redshift-managed storage. That reduces warehouse storage cost for long-term retained data. AWS documentation also recommends using Apache Parquet for Spectrum because Parquet is a columnar format, which allows Redshift Spectrum to read only the columns needed instead of scanning entire text files. This improves performance and lowers query cost.

Option A and D are less cost-effective because auto-copy loads data into the Redshift cluster, which means the company pays to store all historical data in Redshift even though the requirement is indefinite retention of a large volume of IoT data. Option C is worse than B because JSON is a row-oriented text format, and AWS guidance says columnar formats such as Parquet or ORC are preferred for Redshift Spectrum for better scan efficiency and lower cost.

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation—all with minimal code and operational overhead.

“Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.”