AWS Data Engineer Associate Exam Prep

The Data Engineer Associate exam focuses on building and managing data pipelines using AWS analytics services. Data ingestion and transformation is the largest domain at 34%, covering Kinesis (Data Streams, Firehose), AWS Glue (crawlers, jobs, ETL scripts, Data Catalog), EMR, and Step Functions for orchestrating data workflows. You'll need to understand when to use batch vs stream processing and how to design for both.

The data store management domain (26%) tests knowledge of S3 data lake architectures, DynamoDB, Redshift (including Serverless, Spectrum, and data sharing), RDS, and Lake Formation for centralized access control. Expect questions about data formats (Parquet, Avro, JSON, ORC), partitioning strategies, and data lifecycle management with S3 storage classes and Intelligent-Tiering.

Focus on AWS Glue and Kinesis — these two service families dominate the exam. Understand Glue crawlers, the Data Catalog, Glue ETL jobs (both visual and script-based), Glue job bookmarks for incremental processing, and Glue DataBrew for visual data preparation. For Kinesis, know the differences between Data Streams and Firehose, how to handle consumer lag, and how to scale shards.

Redshift is the other major topic: understand distribution styles (EVEN, KEY, ALL), sort keys, Redshift Spectrum for querying S3, and how to optimize query performance with VACUUM and ANALYZE. Lake Formation for data governance is tested throughout the security domain. Use practice exams to identify your weak spots and focus study time accordingly.

Data engineering is one of the fastest-growing roles in tech, and the AWS Data Engineer certification validates the skills employers need most: building scalable data pipelines, managing data lakes, and implementing analytics solutions. Certified AWS data engineers command salaries ranging from $130,000 to $170,000, and demand continues to outpace supply.

This certification is ideal for data engineers working primarily with AWS, ETL developers modernizing on-premises data warehouses, and analytics engineers building data platforms. It pairs well with the Solutions Architect Associate for a comprehensive cloud + data skill set, or with the Machine Learning Engineer Associate for data engineers expanding into ML.