AWS Data Engineer Associate (DEA-C01) Study Guide 2026

About the AWS Data Engineer Associate Exam

Launched in 2023, the AWS Certified Data Engineer Associate (DEA-C01) is AWS's newest associate-level certification. It targets data engineers, ETL developers, and analytics professionals who design, build, and maintain data pipelines on AWS. Unlike the former Big Data Specialty, the DEA-C01 is positioned at the associate level — meaning it is more accessible but still comprehensive.

AWS recommends candidates have at least 2 years of experience with AWS data services and a working knowledge of SQL, Python, or Spark. However, many candidates with strong cloud fundamentals and 3–4 months of focused data engineering study pass without meeting the full experience recommendation.

DEA-C01 Exam Details

Domain 1: Data Ingestion and Transformation (34%)

The largest domain covers the core ETL/ELT patterns on AWS. Key services include AWS Glue (serverless ETL, Glue Data Catalog, Glue DataBrew for no-code transformation), Amazon Kinesis (Data Streams for real-time ingestion, Firehose for delivery to S3/Redshift/OpenSearch), and AWS Database Migration Service (DMS) for migrating on-premises databases to AWS.

You must understand when to use batch vs. streaming ingestion, how to partition data in S3 for performance, and how to handle schema evolution in Glue Data Catalog. Questions frequently test the difference between Kinesis Data Streams (you manage shards/consumers) and Kinesis Firehose (fully managed, 60-second delivery window).

Domain 2: Data Store Management (26%)

Covers storage decisions across the AWS ecosystem. Amazon Redshift is the centerpiece — know distribution styles (KEY, EVEN, ALL), sort keys, Redshift Spectrum (querying S3 from Redshift), and Redshift Serverless. Amazon S3 remains the backbone of data lakes — understand storage classes (S3 Intelligent-Tiering, Glacier), S3 lifecycle policies, and data lake organization patterns (raw/processed/curated zones).

Other tested services: DynamoDB (NoSQL, partition key design, Global Tables for multi-region), Aurora (relational, supports PostgreSQL/MySQL), and AWS Lake Formation (centralized permissions for data lake access control, column/row-level security, governed tables).

Domain 3: Data Operations and Support (22%)

Covers monitoring, orchestration, and cost optimization. AWS Step Functions orchestrates multi-step workflows including Glue jobs, Lambda functions, and EMR clusters. Amazon MWAA (Managed Workflows for Apache Airflow) is tested for complex DAG-based pipelines. CloudWatch is the primary monitoring service — know how to set alarms on Glue job metrics, Redshift query performance, and Kinesis consumer lag.

Cost optimization questions test your knowledge of Redshift reserved nodes vs. on-demand, S3 Intelligent-Tiering breakeven analysis, and EMR Spot Instance strategies. Operational questions often involve troubleshooting — a Glue job that fails because of missing partitions, a Kinesis stream with high GetRecords latency due to too few shards, or a Redshift query with poor performance due to a missing sort key.

Domain 4: Data Security and Governance (18%)

Covers encryption, access control, and compliance. All S3 data should be encrypted at rest — know SSE-S3 (AWS-managed keys), SSE-KMS (customer-managed keys with audit trail via CloudTrail), and SSE-C (customer-provided keys). Lake Formation column/row-level security, AWS Macie for PII detection in S3, and AWS Glue DataBrew's data quality rules are tested.

For governance, understand the difference between AWS Glue Data Catalog (technical metadata) and AWS DataZone (data marketplace with business glossary and data access subscriptions). GDPR/CCPA compliance questions test your knowledge of data masking, tokenization, and deletion workflows.

12-Week DEA-C01 Study Plan

• Weeks 1–2: AWS fundamentals refresher (VPC, IAM, S3 core) + Kinesis deep dive. Build a streaming pipeline in the AWS free tier.
• Weeks 3–4: AWS Glue — ETL scripts, Data Catalog, crawlers, DataBrew. Run at least 5 hands-on Glue jobs.
• Weeks 5–6: Redshift — cluster setup, distribution styles, Spectrum, Serverless. Practice WLM configuration.
• Weeks 7–8: Lake Formation, Athena, EMR. Focus on Lake Formation permissions model — it's heavily tested.
• Weeks 9–10: Step Functions, MWAA, CloudWatch, cost optimization. Build a sample orchestrated pipeline.
• Weeks 11–12: Two to three full practice exams. Review all incorrect answers. Focus on services you scored lowest on in Week 11 during Week 12.

Frequently Asked Questions