AWS Certified Data Engineer – AssociateStudy guide
The associate certification for data engineering on AWS (DEA-C01).
About AWS Certified Data Engineer – Associate (DEA-C01)
AWS Certified Data Engineer – Associate (DEA-C01) is a Associate-level certification from AWS. This page organizes the exam scope into a 4-chapter, 10-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Data Ingestion and Transformation~34%
- Data Store Management~26%
- Data Operations and Support~22%
- Data Security and Governance~18%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://aws.amazon.com/certification/certified-data-engineer-associate/
1Data Ingestion and Transformation
- 1.1Data Ingestion (Batch and Streaming)
Understand batch ingestion (scheduled, bulk) vs. streaming ingestion (continuous, near real-time), and choosing among Kinesis Data Streams / Data Firehose / MSK. The starting point for "Data Ingestion and Transformation" in DEA-C01.
- 1.2ETL and the Data Catalog with AWS Glue
Understand AWS Glue crawlers and the Data Catalog (schema inference, central metadata), serverless Spark ETL jobs, and ETL vs. ELT.
- 1.3Orchestration and Stream Processing
Understand pipeline orchestration (Step Functions / Managed Workflows for Apache Airflow), stream processing (Managed Service for Apache Flink), and lightweight transforms with Lambda.
2Data Store Management
- 2.1Designing an S3 Data Lake
Understand an S3-centric data lake (raw/cleaned/curated layers), partitioning and columnar formats, and cost optimization via storage classes and lifecycle. The starting point for "Data Store Management" in DEA-C01.
- 2.2Data Warehouse and Querying (Redshift, Athena)
Understand Amazon Redshift (columnar warehouse), querying S3 directly with Redshift Spectrum and Athena, and choosing ETL vs. ELT.
- 2.3Choosing Purpose-Built Data Stores
Understand choosing purpose-built stores by access pattern (DynamoDB = NoSQL, RDS/Aurora = relational OLTP, Redshift = OLAP), plus catalogs and schema evolution.
3Data Operations and Support
- 3.1Operating Pipelines and Data Quality
Understand pipeline operations: monitoring (CloudWatch, job success/duration), automation via schedules and retries, and data-quality checks (Glue Data Quality, DLQ). The starting point for "Data Operations and Support" in DEA-C01.
- 3.2Analysis and Visualization
Understand turning data into value: SQL analytics with Athena/Redshift, BI dashboards with Amazon QuickSight, and log/search analytics with OpenSearch.
4Data Security and Governance
- 4.1Data Encryption and Access Control
Understand encryption at rest/in transit (KMS/TLS), least-privilege access control with IAM and bucket policies, and detecting/classifying/masking sensitive data with Macie. The starting point for "Data Security and Governance" in DEA-C01.
- 4.2Governance and Compliance (Lake Formation)
Understand data governance: fine-grained (column/row-level) access management with AWS Lake Formation, auditing with CloudTrail, and classification/data lineage with tags.