Fee $150Level IntermediateAccess Open to anyoneDifficulty 3/5
1. The quick verdict
Worth it if you build and run data pipelines on AWS, including the ones that feed ML and generative AI systems. Be clear on what it is: a data engineering certification that supports AI work, not an AI certification. It does not test model training, inference or generative AI.
The one thing to know before you register: AWS recommends 2–3 years of data engineering experience, so this is not a starter exam.
2. Our ratings
3. At a glance
| Field | Details |
|---|---|
| Level | Associate |
| Access | Open to anyone |
| Exam fee | US$150 |
| Retake policy | Full fee each attempt, 14-day wait, no attempt limit |
| Format | Pearson VUE, online proctored or test centre. 65 questions (50 scored, 15 unscored), 130 minutes. Multiple choice and multiple response. English, Japanese, Korean, Simplified Chinese. |
| Passing score | 720 on a 100–1,000 scaled score |
| Coding needed | Reading SQL and pipeline config. No writing code, and no language-specific syntax questions. |
| Formal prerequisites | None |
| Realistic prerequisites | 2–3 years in data engineering or data architecture, and 1–2 years hands-on with AWS services (AWS’s own recommendation). |
| Validity & renewal | 3 years. Recertify by passing the latest version, passing Generative AI Developer – Professional, or a paid Skill Builder maintenance option. 50% discount voucher for recertification exams. |
| Estimated prep time | Data engineer on AWS now: 25–40 hours. Data engineer new to AWS: 50–70 hours. |
| Official exam guide | DEA-C01 exam guide (AWS) |
Prep-time estimates are our own judgment, not an AWS figure.
4. What the exam tests
| Domain | Weight | What it means in plain language |
|---|---|---|
| Data Ingestion and Transformation | 34% | Getting data in (batch and streaming) and transforming it with ETL tools such as Glue, EMR and Lambda. |
| Data Store Management | 26% | Choosing and managing storage: S3 data lakes, Redshift, DynamoDB and catalogues. |
| Data Operations and Support | 22% | Running pipelines reliably: orchestration, monitoring, data quality and troubleshooting. |
| Data Security and Governance | 18% | Encryption, access control, logging and data governance. |
Out of scope: ML training and inference, programming-language syntax, and drawing business conclusions from data.
5. Who should take it, and who shouldn’t
Take it if you are:
- A data engineer or data architect working on AWS.
- An ML or AI engineer who owns the data pipelines behind your models and wants that skill recognized.
- A backend engineer moving into data platform work.
Skip it or wait if you are:
- Looking for an AI credential. This one will not signal AI skill; take ML Engineer – Associate or Generative AI Developer – Professional.
- Early in data engineering. Build pipelines for a year or two first.
- Not on AWS. The concepts transfer, but most questions are service-specific.
6. What to expect after passing
- What it signals: You can build, run and secure data pipelines and stores on AWS.
- What it doesn’t prove: Anything about AI or ML modelling. Do not present it as an AI certification.
- Where it’s asked for: Data engineer job postings on AWS stacks, and AWS partner requirements. It also strengthens an AI profile when paired with an AI certification.
- Natural next step: ML Engineer – Associate to move toward ML, or Generative AI Developer – Professional for RAG and foundation-model data work.
7. How to prepare
- Official: The DEA-C01 exam guide, the AWS Skill Builder exam prep plan, and documentation for Glue, Kinesis, Redshift, Lake Formation and Step Functions.
- Practice: Use scenario questions that ask for the best service under cost, latency or governance constraints. A good set explains why the near-miss services are wrong.
Hands-on:
- Build a batch pipeline from S3 through Glue into Redshift or Athena (Ingestion, Store Management).
- Add a streaming feed with Kinesis and orchestrate both with Step Functions (Ingestion, Operations).
- Lock it down with Lake Formation permissions, encryption and CloudTrail logging (Security and Governance).
8. Compare with
- AWS Certified Machine Learning Engineer – Associate (MLA-C01), $150: Picks up where this stops: preparing data for models, then training and deploying them.
- AWS Certified Generative AI Developer – Professional (AIP-C01), $300: Includes data management for foundation models and RAG, at a professional level.
See all AWS guides → · Compare all AI certifications →
9. FAQ
Is the AWS Data Engineer certification an AI certification?
No. It covers the data pipelines AI systems depend on, but it does not test ML or generative AI. Pair it with an AI certification if you want to signal AI skills.
Data Engineer or ML Engineer first?
If your work is mostly pipelines and storage, Data Engineer. If you train and deploy models, ML Engineer. Many ML engineers take both.
Do I need to know a programming language?
You need general programming and SQL concepts, but the exam does not test language-specific syntax.
How do I keep it active?
It lasts 3 years. Passing Generative AI Developer – Professional also renews it.
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Sources
- AWS Certified Data Engineer – Associate, AWS: fee, format, languages, validity
- DEA-C01 exam guide, AWS: domains, weights, passing score, out-of-scope items
- AWS Recertification, AWS: renewal options
- After Testing policy, AWS: retake rules