AWS Certified Data Engineer - Associate (DEA-C01) Practice Questions

While SAA-C03 covers broad architectural design across all AWS services, DEA-C01 is purpose-built for data engineers. It goes deeper into data-specific services like AWS Glue, Lake Formation, Kinesis, and Redshift, with a focus on pipeline implementation, data modeling, and ETL/ELT workflows. SAA-C03 is a prerequisite for many, but DEA-C01 is the certification that validates you can build and operate data pipelines, not just design architectures. Many professionals earn SAA-C03 first, then pursue DEA-C01 to specialize.

The exam heavily tests data ingestion and processing services including AWS Glue (ETL jobs, crawlers, Data Catalog), Amazon Kinesis (Data Streams, Firehose, Analytics), Amazon S3 (as a data lake with partitioning and compression), Amazon Redshift (warehousing, Spectrum, Redshift Serverless), and AWS DMS (database migration). For data stores, expect DynamoDB, RDS, OpenSearch Service, and Amazon EMR. Security and governance topics focus on Lake Formation, IAM, KMS, and AWS Macie. Understanding when to use each service for specific data workloads is critical.

AWS recommends 2–3 years of data engineering experience with 1–2 years of hands-on AWS experience. You should understand ETL/ELT pipeline concepts, data modeling (star schema, data vault, 3NF), SQL querying, and basic programming concepts (Python or Scala are helpful). Familiarity with Git for source control and general networking/storage concepts is also recommended. The exam does NOT require ML training/inference skills or deep programming language syntax knowledge. Our practice tests help bridge any knowledge gaps.

Our product delivers 400+ practice questions organized across all four domains with correct weightings. Each question includes detailed explanations that explain the reasoning behind correct and incorrect answers, designed to build the analytical decision-making skills data engineers need daily. You get full-length simulation exams (65 questions, 130 minutes) that replicate the real Pearson VUE testing experience, domain-wise practice modules for focused study on weaker areas, and a downloadable PDF study guide featuring AWS service comparison tables, pipeline architecture diagrams, and key concept summaries for quick reference.

Study time varies by background. Those with 2+ years of data engineering experience typically need 4-6 weeks (60-80 hours). Candidates with AWS experience but new to data engineering may need 8-12 weeks (100-140 hours). We recommend a phased approach: (1) Review the official exam guide and in-scope services list; (2) Complete AWS Skill Builder's DEA-C01 Exam Prep digital courses; (3) Practice hands-on with relevant AWS services; (4) Use our practice tests for domain-wise assessment; (5) Take full-length simulation exams; (6) Focus final review on weak domains identified through practice test performance.

Yes — Data Ingestion and Transformation is the highest-weighted domain at 34%, and AWS Glue is at the center of it. You need to understand Glue ETL jobs (Python Shell, Spark), Glue crawlers and the Data Catalog, Glue Studio for visual ETL, and Glue DataBrew for data preparation. Beyond Glue, expect questions on Kinesis for streaming ingestion, DMS for database migration, Step Functions for pipeline orchestration, and best practices for data format selection (Parquet, ORC, Avro) and partitioning strategies. Knowing how to build end-to-end data pipelines is essential for exam success.

The DEA-C01 is one of AWS's newest Associate certifications and addresses the rapidly growing demand for skilled data engineers. As organizations increasingly build data-driven applications and AI/ML pipelines, certified data engineers are critical for designing and operating the underlying data infrastructure. This certification positions you for roles such as Data Engineer, Analytics Engineer, Data Platform Architect, and Big Data Engineer. It also serves as a natural stepping stone to the AWS Certified Data Analytics - Specialty (DAS-C01) or Machine Learning Engineer - Associate (MLA-C01) for those advancing in the data and AI space.