AWS Certified Data Engineer - Associate
DEA-C01
The AWS Certified Data Engineer - Associate (DEA-C01) certification validates the ability to build and run data pipelines on AWS: ingesting and transforming data, orchestrating pipelines, choosing and modelling data stores, operating and monitoring what you ship, and securing and governing the data throughout. The exam is 65 questions in 130 minutes, scored 100-1,000 with a minimum passing score of 720 and compensatory scoring, and there are no case studies. Guide Version 1.1 brings the exam up to date with open table formats such as Apache Iceberg, vector index types, Amazon Bedrock knowledge bases, LLM invocation inside pipelines, and Amazon SageMaker Catalog and Unified Studio. Performing ML training and inference is out of scope, but calling a model from a pipeline is not, and SQL is tested throughout.
65
130 min
720/ 1000
247+
Target Audience
Data engineers, analytics engineers and ETL developers who already build pipelines and want to prove it on AWS. AWS recommends 2-3 years of data engineering experience plus 1-2 years hands-on with AWS services. Also suitable for data analysts, BI developers and backend engineers moving into pipeline ownership, and for SQL-strong practitioners formalising their cloud skills.
Prerequisites
No prerequisite certification. You should be comfortable with ETL end to end, with SQL (SELECT with JOINs, query structuring and optimization), and with the core AWS data stack — S3, AWS Glue, Amazon Redshift, Amazon Athena, AWS Lambda, Amazon Kinesis, AWS Step Functions, IAM and AWS KMS. Familiarity with Git and with infrastructure as code helps. Language syntax recall is not tested, so depth in any one programming language matters less than knowing which service fits which constraint.
Question Types
Language & Recognition
Official Exam Languages
PasslyExam Languages
Recognition Scope
Globally recognized and broadly applicable across countries.
Exam Domains
Data Ingestion and Transformation
Data Store Management
Data Operations and Support
Data Security and Governance
Recommended Study Plan
- Week 1: Lock down the four domains and their weights from the 2026 Version 1.1 guide — data ingestion and transformation 34%, data store management 26%, data operations and support 22%, data security and governance 18%. Much of the material online is still 1.0, so check what came in (Apache Iceberg and open table formats, vector index types HNSW and IVF, Amazon Bedrock knowledge bases, LLM invocation inside pipelines, SageMaker Catalog and Unified Studio) and strike what went out: AWS SCT, Cloud9 and CodeCommit.
- Week 2: Build a table of the decision axes: streaming versus batch ingestion; which of Kinesis Data Streams, Amazon Data Firehose and Amazon MSK can replay; Glue versus EMR versus Lambda versus Redshift for transformation; and S3, Redshift, RDS/Aurora, DynamoDB, MemoryDB and OpenSearch for storage. Solve 20–25 questions a day and tag each miss by which axis you read wrong.
- Week 3: Drill operations and security as scenarios: Step Functions Standard versus Express, how MWAA and Glue workflows divide the job, the CloudWatch Logs Insights query pipeline, Lake Formation fine-grained permissions, Secrets Manager rotation, and the fact that an access point policy and the bucket policy both apply.
- Final 5–7 days: Run timed mocks to fix your pacing across the 130 minutes. Make a last pass over documented numbers and defaults: S3 minimum storage durations (30/30/90/90/180 days) and the 30-day minimum between chained transitions, Redshift DISTSTYLE selection, and the fact that enhanced fan-out is not the Kinesis default.
Question Validation Process
Try the practice set directly and judge real-exam similarity yourself.
Frequently Asked Questions
Q. What does the DEA-C01 exam validate?
A. It validates your ability to build and operate data pipelines on AWS, at Associate level. The exam is 65 questions (50 scored, 15 unscored) in 130 minutes, scored 100–1,000 with a minimum passing score of 720 and compensatory scoring, so there is no per-domain cut. It costs USD 150 and has no case studies. It is offered in English, Japanese, Korean and Simplified Chinese.
Q. How much experience is recommended?
A. AWS recommends 2–3 years of data engineering experience plus 1–2 years hands-on with AWS services. There is no prerequisite certification. You need ETL end to end, SQL (SELECT with JOINs, query structuring and optimization), and the core stack: S3, Glue, Redshift, Athena, Lambda, Kinesis, Step Functions, IAM and KMS. Language syntax is not tested, so knowing which service fits which constraint matters more than depth in any one language.
Q. Where do candidates lose the most points?
A. On documented defaults and numbers. Recurring misses: thinking enhanced fan-out is the Kinesis default (it is a shared-throughput standard consumer), thinking Amazon Data Firehose can replay, the S3 minimum storage durations and the 30-day minimum between chained transitions, choosing a plain view where a materialized view is required for a repeated expensive aggregate, and the DISTSTYLE choice for a small dimension table. Tagging your wrong answers by which of these you misread speeds up improvement.
Q. Why does the syllabus differ from much of the material online?
A. Because the exam guide moved to Version 1.1 while a lot of material is still 1.0 — even one of the official PDF mirrors still serves 1.0. Version 1.1 added Apache Iceberg and open table formats, vector index types (HNSW, IVF), Amazon Bedrock knowledge bases, LLM invocation inside pipelines, Amazon SageMaker Catalog and Unified Studio, plus Aurora, Amazon Q, Kendra and AWS Data Exchange. It removed AWS Schema Conversion Tool (AWS SCT), Cloud9 and CodeCommit, so learn heterogeneous schema conversion as AWS DMS Schema Conversion.
Q. What mock score is considered stable?
A. A practical benchmark is 75–80%+ across your latest 3 mocks without collapsing in data ingestion and transformation (34%) or data store management (26%). The cut is 720, but as a scaled score it does not map directly to a percentage correct, so watch the trend and the fall in repeat errors rather than a single high score.
Q. How do practice questions and the timed mock exam differ?
A. Practice questions are solved by topic with answer explanations to shore up weak areas; the timed mock exam is taken like the real thing. Both use original questions with AI tutor explanations.
Q. Is this similar to exam dumps?
A. PasslyExam does not provide leaked dumps. Our content is built from official guides and public exam objectives, then tuned to reflect realistic question patterns and difficulty distribution. The focus is practical readiness with explanation-based learning, not memorization of leaked items.
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