AWS Data Engineer Associate (DEA-C01)
Validate your ability to design and maintain data pipelines on AWS
Exam Quick Facts
Exam Code
DEA-C01
Questions
65
Duration
130 minutes
Passing Score
720/1000 (~72%)
Cost
$150 USD
Valid For
3 years
Last Updated
2024
Difficulty
Intermediate
AWS Certified Data Engineer – Associate Cheat Sheet (DEA-C01)
Every in-scope ingestion, transformation, storage and governance service the DEA-C01 exam actually tests — organised by the four exam domains with decision tables and runbooks
Certification Overview
The AWS Certified Data Engineer Associate (DEA-C01) validates your skills in designing, building, securing, and maintaining data pipelines on AWS. Launched in 2024, this certification fills a critical gap in the AWS certification portfolio by targeting data engineers who work with ETL processes, data lakes, data warehouses, and analytics pipelines.
Data engineering has become one of the fastest-growing roles in tech, and AWS is the leading cloud platform for data workloads. The DEA-C01 tests your ability to ingest data from diverse sources, transform it efficiently, orchestrate complex data workflows, and ensure data quality and governance — all using AWS-native services.
Unlike the Solutions Architect certification that focuses on infrastructure design, the Data Engineer Associate zeroes in on the data lifecycle: how data moves from source systems through processing layers into analytics and machine learning platforms. If you spend your days working with Glue, Redshift, Athena, or Kinesis, this certification is built for you.
Who Should Take This Exam?
The DEA-C01 is designed for data engineers, ETL developers, data platform engineers, and analytics engineers with at least one year of hands-on AWS experience. If your daily work involves designing data pipelines, building data lakes on S3, running queries in Athena, or managing data warehouses in Redshift, this certification validates those skills.
It's also valuable for software engineers transitioning into data engineering, or data analysts looking to move into more technical data roles. The certification signals to employers that you understand both the theory and practice of modern data engineering on AWS.
Exam Domains
Data Ingestion and Transformation
The largest domain, covering how to ingest data from various sources using services like Kinesis, MSK, Database Migration Service, and AWS Transfer Family. Also covers data transformation using Glue ETL, Glue DataBrew, Lambda, and EMR. You need to understand batch vs. streaming ingestion patterns and how to optimize transformation jobs.
Data Store Management
Tests your knowledge of choosing and managing the right data store for different use cases: S3 for data lakes, Redshift for data warehousing, DynamoDB for key-value stores, RDS/Aurora for relational data, and OpenSearch for search and log analytics. Covers data modeling, partitioning strategies, and storage optimization.
Data Operations and Support
Covers data pipeline orchestration using Step Functions and MWAA (Managed Workflows for Apache Airflow), monitoring with CloudWatch, data quality checks, pipeline troubleshooting, and operational best practices. Understanding how to automate, monitor, and recover data pipelines is essential.
Data Security and Governance
Tests your ability to implement data access controls, encryption, data cataloging with Glue Data Catalog and Lake Formation, data lineage, compliance requirements, and PII handling. Understanding Lake Formation permissions and Glue Data Catalog metadata management is particularly important.
Ready to test your DEA-C01 knowledge?
Practice with our free Data Engineer Associate (DEA-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice ExamStudy Strategy
Start with hands-on AWS data services if you haven't already. Build a small data pipeline: ingest data into S3, catalog it with Glue, transform it with a Glue ETL job, and query the result with Athena. Then load it into Redshift for warehousing. This end-to-end exercise covers the core workflow the exam tests.
Focus most of your study on Data Ingestion and Transformation (34%) — it's the largest domain. Make sure you understand the differences between Kinesis Data Streams vs. Kinesis Data Firehose, when to use Glue ETL vs. EMR, and how to handle schema evolution. Also ensure you know the trade-offs between batch and real-time processing patterns.
Practice exams are crucial for this cert because the questions are scenario-heavy. You'll be presented with a data architecture problem and need to choose the most efficient or cost-effective solution. Budget 4-8 weeks of study, and aim for 80%+ on practice tests before booking the real exam.
Key AWS Services to Know
- AWS Glue — ETL service with crawlers, Data Catalog, and DataBrew
- Amazon S3 — data lake foundation, storage classes, lifecycle policies
- Amazon Redshift — cloud data warehouse with Redshift Spectrum
- Amazon Athena — serverless SQL queries on S3 data
- Amazon Kinesis — real-time data streaming (Streams, Firehose, Analytics)
- Amazon MSK — managed Apache Kafka for streaming
- AWS Step Functions — workflow orchestration for data pipelines
- Amazon MWAA — managed Apache Airflow for complex workflows
- AWS Lake Formation — data lake access control and governance
- Amazon EMR — managed Hadoop/Spark for big data processing
- AWS DMS — Database Migration Service for data replication
- Amazon DynamoDB — NoSQL database for high-throughput workloads
Career Impact
Data engineering is one of the highest-demand, highest-paying specializations in tech. The AWS Data Engineer Associate certification positions you for roles like data engineer, data platform engineer, analytics engineer, and ETL developer — roles that typically pay $130K-$180K depending on location and experience.
As organizations increasingly build data lakes and modern data platforms on AWS, certified data engineers are in short supply. This certification differentiates you from general cloud practitioners and signals deep expertise in the data domain. It's particularly valuable when combined with the Solutions Architect Associate, showing both infrastructure design and data pipeline expertise.
Frequently Asked Questions
What experience do I need for the Data Engineer Associate?
AWS recommends 2-3 years of data engineering experience and 1-2 years of hands-on AWS data services experience. You should be comfortable with Glue, S3, Redshift, Athena, and Kinesis at a minimum.
How does the DEA-C01 differ from the Solutions Architect Associate?
The SAA covers broad infrastructure design, while the DEA focuses specifically on data pipelines, data lakes, warehousing, and analytics workflows. There is some overlap in services like S3 and IAM, but the perspective is different.
Do I need to know Apache Spark or Hadoop for this exam?
At a conceptual level, yes. You should understand when to use EMR with Spark vs. Glue ETL, and the basics of distributed data processing. You won't need to write Spark code, but you need to know when it's the right tool.
Is the Data Engineer Associate harder than the Solutions Architect Associate?
They're comparable in difficulty but test different domains. If you're a data engineer, the DEA may feel easier because it aligns with your daily work. The key challenge is the breadth of AWS data services you need to know.
Ready to test your DEA-C01 knowledge?
Practice with our free Data Engineer Associate (DEA-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice Exam