Free study guide · 2026 edition
AWS Certified Data Engineer — Associate
DEA-C01 is the AWS data engineering certification that replaced the retiring Data Analytics Specialty (DAS-C01). It targets data engineers building ingestion, storage, and transformation pipelines on AWS for analytics and ML workloads.
Last updated: May 15, 2026Author: FactualMinds AWS ArchitectsReviewed by: Palaniappan P · AWS Solutions Architect — Professional
Exam code
DEA-C01
Duration
130 minutes
Questions
65
Cost
$150 USD
Passing score
720 / 1000
Format
Multiple choice and multiple response
Valid for
3 years
Recommended experience
2–3 years of data engineering experience and 1+ year of hands-on AWS data services
Exam domains
4 domains · 24 topics
1 Data Ingestion and Transformation
34%
Data Ingestion and Transformation
- Batch ingestion: AWS Glue, EMR Serverless, AWS DataSync, AWS Transfer Family
- Streaming ingestion: Kinesis Data Streams, Kinesis Data Firehose, Amazon MSK, Amazon Managed Service for Apache Flink
- Change data capture: DMS, Aurora zero-ETL to Redshift, DynamoDB zero-ETL
- Transformation: Glue Spark jobs, Glue Studio visual ETL, dbt on Glue / Redshift, EMR Serverless Spark
- Schema management: Glue Data Catalog, Glue Schema Registry, Iceberg schema evolution
- Data formats: Parquet, Apache Iceberg (via S3 Tables), Avro, ORC; compression and partitioning
2 Data Store Management
26%
Data Store Management
- S3 storage classes: Standard, IA, One Zone, Glacier tiers; lifecycle policies
- S3 Tables (managed Apache Iceberg) for lakehouse workloads
- Amazon Redshift architecture: leader nodes, compute nodes, RA3 with managed storage, Redshift Serverless
- Redshift workload management, materialized views, federated queries, Spectrum
- DynamoDB capacity modes, partition keys, GSI/LSI, DAX, DynamoDB Streams
- OpenSearch Service: domains, indexes, ISM policies, OpenSearch Serverless trade-offs
- Aurora and RDS for transactional data feeding analytics; Aurora DSQL for distributed Postgres
3 Data Operations and Support
22%
Data Operations and Support
- Orchestration: Step Functions, MWAA (Apache Airflow), EventBridge Scheduler, Glue Workflows
- Monitoring: CloudWatch, Application Signals, EMR Studio, Glue job monitoring, Redshift Advisor
- Cost optimization: Athena query cost, partition pruning, Glue worker tuning, Redshift concurrency scaling
- Data quality: Glue Data Quality (DQDL), deequ, Great Expectations on Glue
- Logging and alerting: CloudWatch Logs Insights, Athena over CloudTrail data, GuardDuty findings
4 Data Security and Governance
18%
Data Security and Governance
- AWS Lake Formation: row, column, and cell-level access; tag-based access control
- Encryption: KMS-managed keys, S3 server-side encryption variants, Redshift encryption
- PII detection: Macie scans, Glue PII detection transforms
- Compliance for analytics: HIPAA-eligible data lake on Glue + S3, SOC 2 evidence collection
- Data sharing: Redshift data sharing, Lake Formation cross-account, AWS Data Exchange
- Audit trails: CloudTrail for data services, Lake Formation logging, dataset-level audit
Why DEA-C01 exists
AWS retired the Data Analytics Specialty (DAS-C01) in 2024 and replaced it with the Data Engineer Associate (DEA-C01) to better reflect what data engineers actually do on AWS in 2024–2026:
- Lakehouse over warehouse-only — S3 Tables (managed Iceberg), Lake Formation, Athena, EMR Serverless feature heavily.
- Streaming alongside batch — Kinesis, MSK, and Managed Flink are first-class objectives.
- Zero-ETL patterns — Aurora to Redshift, DynamoDB to Redshift, RDS to OpenSearch show up.
- Governance — Lake Formation tag-based access, data quality, and PII detection are tested deeply.
Recommended 5-week study plan
Week 1 — Ingestion (10 hours) Build a Glue job from S3 to Iceberg on S3 Tables. Set up Kinesis Data Firehose to Redshift. Try Aurora zero-ETL into Redshift.
Week 2 — Storage (8 hours) Walk through S3 lifecycle policies, S3 Tables with Athena queries, Redshift Serverless. Compare DynamoDB capacity modes on a sample workload.
Week 3 — Transformation + orchestration (10 hours) Build a Step Functions pipeline that triggers a Glue job, runs a Redshift stored procedure, and notifies via SNS. Try MWAA on a tutorial DAG.
Week 4 — Governance + security (8 hours) Configure Lake Formation tag-based access control for a Glue catalog. Run Macie on a sample bucket. Set up a Glue Data Quality DQDL rule set.
Week 5 — Practice exams + weak areas (8 hours) Two Tutorial Dojo practice sets. Review every wrong answer. Re-read the AWS Well-Architected Analytics Lens.
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Frequently asked questions
Should I take DEA-C01 or wait for an updated DAS-C01?
DAS-C01 (Data Analytics Specialty) has been retired by AWS — DEA-C01 is the replacement and the only currently-offered AWS data engineering certification at the Associate level. Take DEA-C01.
How is DEA-C01 different from MLA-C01?
DEA-C01 focuses on data pipelines — ingestion, storage, transformation, governance — that feed analytics and ML downstream. MLA-C01 focuses on ML model lifecycle — feature engineering, training, deployment, monitoring. There is overlap on data preparation and feature engineering. If you build pipelines, take DEA-C01; if you build models, take MLA-C01; if you do both, take DEA-C01 first.
What does DEA-C01 add over SAA-C03 for a data team?
SAA-C03 covers data services at architect breadth — what each service does, when to use it. DEA-C01 goes deeper into operating those services: Glue worker tuning, Redshift WLM queue setup, Iceberg schema evolution, Lake Formation tag-based access, DQDL data quality rules. If your day job is building data pipelines, DEA-C01 is the more useful credential.
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