AWS Certified Data Engineer - Associate (DEA-C01)¶

Overview¶

The AWS Certified Data Engineer - Associate (DEA-C01) validates the skills needed to ingest, transform, store, operate, and secure data pipelines on AWS. It replaces both the retired AWS Data Analytics Specialty (DAS-C01) and the retired AWS Database Specialty (DBS-C01), consolidating data-engineering expertise into a single Associate-tier credential.

This cert targets data engineers, analytics engineers, and ETL developers with 1-2 years of hands-on experience building data pipelines on AWS, plus 2-3 years of broader data engineering experience.

Quick Reference¶

📖 Official AWS Data Engineer - Associate page

Exam Domains¶

Domain 1 - Data Ingestion and Transformation (34%)¶

The largest and most weighted domain. You must understand how to:

• Ingest data from streaming, batch, and database sources (Kinesis Data Streams, Kinesis Data Firehose, MSK, DMS, AppFlow, Glue, Lambda)
• Transform data with AWS Glue ETL, Amazon EMR (Spark, Hive, Presto), Lambda, Step Functions
• Handle late-arriving data, schema evolution, idempotency, and replays
• Choose between micro-batch, batch, and streaming patterns
• Orchestrate workflows with Step Functions, MWAA (Managed Apache Airflow), and EventBridge

Domain 2 - Data Store Management (26%)¶

Choosing and operating the right data store for the workload:

• Object storage (S3 storage classes, partitioning strategies, file formats - Parquet, ORC, Avro, JSON)
• Data warehouses (Amazon Redshift, Redshift Serverless, RA3 nodes, materialized views, distribution and sort keys)
• Operational databases (RDS, Aurora, DynamoDB, ElastiCache)
• Data catalogs and lakes (AWS Glue Data Catalog, Lake Formation, Iceberg / Hudi / Delta on S3)
• Search and analytics stores (OpenSearch, OpenSearch Serverless)

Domain 3 - Data Operations and Support (22%)¶

Running data pipelines reliably in production:

• Athena for ad-hoc and pipeline querying (Athena workgroups, partition projection, CTAS / INSERT INTO)
• QuickSight for analytics and visualization
• CloudWatch metrics, alarms, Logs Insights, and EventBridge for pipeline monitoring
• Cost monitoring and FinOps (S3 Storage Lens, Cost Explorer, billing alarms)
• Quality and observability (Glue Data Quality, Deequ, custom validation steps)
• Recovery from failures (DLQs, SQS retries, EMR step retries, Step Functions error handling)

Domain 4 - Data Security and Governance (18%)¶

Securing data end to end:

• IAM (least privilege, identity-based vs resource-based policies, Lake Formation permissions, Glue resource policies)
• Encryption (KMS keys, S3 encryption (SSE-S3, SSE-KMS, DSSE-KMS), RDS / Redshift / DynamoDB encryption, in-transit TLS)
• Network controls (VPC endpoints / PrivateLink for Glue, S3, Redshift, OpenSearch; subnets and security groups)
• Data governance (Lake Formation tag-based access control, row / column / cell-level security, data sharing across accounts)
• Compliance and discovery (Macie for sensitive-data discovery, AWS Config for posture, CloudTrail for audit)

Study Materials in This Guide¶

Recommended Study Time¶

Plan on 2-3 hours per day of focused study.

Prerequisites and Recommended Prior Certs¶

DEA-C01 has no formal prerequisites. AWS recommends:

• AWS Cloud Practitioner (CLF-C02) if you are new to AWS
• AWS Solutions Architect Associate (SAA-C03) strongly complementary for architectural context
• General data engineering experience: SQL, Python or Java/Scala, Spark, ETL fundamentals

If you held the retired DAS-C01 or DBS-C01, your DEA-C01 prep will compress significantly. Most DAS-C01 material maps directly. DBS-C01's database operations content is partially covered.

Key Differences vs. Retired DAS-C01 and DBS-C01¶

• Level: Associate (vs. Specialty for both retired exams)
• Scope: Broader than DAS-C01 (adds operational databases, more Glue / DMS coverage) and broader than DBS-C01 (adds streaming, lakehouse, analytics)
• Modern services: Includes current Glue (3.0/4.0/5.0 capabilities), Redshift Serverless, Athena V3 engine, Lake Formation tag-based access, Iceberg/Hudi/Delta lakehouse formats
• Career alignment: Maps to today's "data engineer" job titles, not the older "DBA" or "data analytics specialist" framing

Companion Materials in This Repo¶

• Practice Questions - sample questions
• AWS CLI Cheat Sheet - common Glue, S3, Redshift commands
• Data Engineer Roadmap - career path that includes DEA-C01
• Data Pipeline Architecture Pattern - cross-cloud pipeline reference
• Lakehouse Architecture Pattern - lakehouse design

Exam Day Tips¶

1. Watch domain weights. Domain 1 (Ingestion/Transformation) is 34%; weak coverage there can sink you. Domain 4 (Security/Governance) is only 18% but the questions are precise.
2. Read every word. Scenario questions hide constraints (cost target, latency SLA, compliance requirement) in the body. Skim at your peril.
3. Eliminate first. Most questions have 1-2 obviously wrong options. Knock those out, then reason through the remaining options.
4. Pick the AWS-native solution. When in doubt, the answer that uses managed AWS services (Glue, Lake Formation, Athena) over self-managed Spark on EC2 is usually correct.
5. Cost matters. Many questions have a "most cost-effective" qualifier. S3 lifecycle policies, Athena partition projection, and Spot pricing on EMR are common cost levers.
6. Time: ~2 minutes per question. Flag hard ones and come back. 130 minutes for 65 questions.

Good luck.