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

Option A minimizes administration because it keeps one base IAM role for the pipeline and enforces regional locality at session time by attaching a session policy when the broker calls AssumeRole. This aligns with the security best practice of applying only what is necessary, because permissions should be limited to the exact scope required for the session. The material explicitly emphasizes the principle of least privilege, stating that permissions “should apply only to what is necessary,” and the most appropriate approach is to grant only the minimum actions needed for the job.

With session policies, the organization avoids creating and maintaining separate per-Region roles (Option B), avoids managing users and group memberships for federated sessions (Option C), and avoids individually attaching policies per engineer (Option D). Instead, the broker can dynamically apply a Region-specific restriction for each login, ensuring the temporary credentials are valid only for the engineer’s working Region. This design reduces ongoing operational burden while still enforcing strict locality controls through temporary, scoped credentials and least-privilege boundaries.

AWS Glue Data Quality is designed specifically to validate data as part of ETL pipelines with minimal cost and operational overhead. By creating a custom data quality rule, the data engineer can enforce business logic such as ensuring that the shipping date is later than the order date and that the delivery date is later than the shipping date.

Glue Data Quality rules run natively within AWS Glue ETL jobs, eliminating the need for additional services, duplicate queries, or post-processing steps. The service supports automatically identifying failed records and routing them to a separate Amazon S3 bucket, which directly satisfies the requirement to isolate invalid orders.

Using AWS Glue DataBrew introduces additional profiling and transformation steps that are unnecessary for a simple rule-based validation. Athena-based validation would require scanning data repeatedly, increasing query costs and adding complexity. AWS Glue crawlers only discover schema metadata and cannot enforce data quality rules.

From a cost and architecture perspective, Glue Data Quality integrates directly into existing Glue pipelines, avoids additional compute usage, and provides centralized monitoring of data quality metrics. Therefore, Option C is the most cost-effective and exam-aligned solution.

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.

Amazon S3 Intelligent-Tiering is designed to optimize storage costs by automatically moving objects between access tiers based on access patterns. Since Apache Iceberg works with S3 storage, using Intelligent-Tiering provides cost-efficiency without the need for custom development or jobs.

Option B improves performance but doesn ' t optimize cost automatically.

Option C is not a real AWS Glue feature – Glue does not automatically optimize Iceberg storage.

Option D requires custom development effort, which is contrary to the requirement.

“S3 Intelligent-Tiering is ideal for data lakes and analytics use cases that access data irregularly.”

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.

This solution reduces operational complexity because you don ' t need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.

Option C is the best answer because Amazon MWAA is a fully managed Apache Airflow service built specifically for workflow orchestration. Airflow uses Directed Acyclic Graphs (DAGs) to model task dependencies, which directly satisfies the requirement for dependency management. Amazon MWAA also supports orchestration across AWS analytics services such as Amazon S3, Amazon Redshift, and Amazon EMR, making it a natural fit for complex ETL pipelines. AWS documentation describes workflow definitions, tasks, and Airflow-based orchestration as core MWAA concepts.

This choice is stronger than A because the question specifically asks for orchestration of complex ETL workflows with dependency management, retries, and monitoring across multiple data-processing systems. While AWS Step Functions is an orchestration service, the option specifies an express workflow, and AWS states that Express Workflows are designed for high-volume, short-duration runs and can run for only up to five minutes, which is often not the best fit for longer-running ETL jobs on EMR and Glue.

The uploaded study material also points to orchestration services for dependent ETL jobs and identifies both Step Functions and workflow orchestration concepts as core exam knowledge, but for this exact multi-step ETL orchestration pattern, MWAA with DAGs is the most precise match.

AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.

Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.

Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.

Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.

“You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3.”

Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database “designed for highly dynamic datasets with frequent read and write operations,” providing low-latency performance at any scale—which directly matches the latency and traffic-spike requirements.

DynamoDB’s key-value and document model fits “product data that has variable attributes” because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB’s primary key access pattern. The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.

Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text/query relevance rather than primary-key transactional access patterns. Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high-QPS query workloads.

Problem Analysis:

The company must log all user activities and connection activities in Amazon Redshift for security compliance.

Key Considerations:

Redshift supports audit logging, which can be configured to write logs to an S3 bucket.

S3 provides durable, scalable, and cost-effective storage for logs.

Solution Analysis:

Option A: S3 for Logging

Standard approach for storing Redshift logs.

Easy to set up and manage with minimal cost.

Option B: Amazon EFS

EFS is unnecessary for this use case and less cost-efficient than S3.

Option C: Aurora MySQL

Using a database to store logs increases complexity and cost.

Option D: EBS Volume

EBS is not a scalable option for log storage compared to S3.

Final Recommendation:

Enable Redshift audit logging and specify an S3 bucket as the destination.

Amazon Redshift Audit Logging

Storing Logs in Amazon S3

Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.

Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.

The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format. References:

Amazon Athena

Choosing the Right Data Format

AWS Glue

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena