DEA-C01 asks a single running question: can you follow data from where it’s born to where it’s used, and secure it the whole way there? The official guide covers implementing data pipelines; monitoring, troubleshooting, and optimizing cost and performance; choosing data stores; designing data models; managing data lifecycles; ensuring data quality; and applying authentication, authorization, encryption, privacy, governance, and logging.

A data-engineering decision exam

The AWS certification page currently lists a 130-minute exam with 65 multiple-choice or multiple-response questions — 50 scored, 15 unscored, a 100–1,000 scaled score, and a minimum passing score of 720. The target-candidate profile describes equivalent experience as helpful, not a formal prerequisite. AWS states the credential is valid for 3 years; confirm the current recertification path before it lapses.

DEA-C01 Domains: What Each One Is Really Testing

Data Ingestion & Transformation

The largest domain, and it’s asking whether you can move data from a source, apply transformation logic correctly, and orchestrate the steps in between using the right programming concepts for a pipeline — not a one-off script.

Data Store Management

This domain tests whether you choose a data store based on the access pattern, not habit. Relational, key-value, object, warehouse, lake, and open-table approaches each solve a different shape of problem.

Data Operations & Support

Operationalizing a pipeline is a different skill from building it once. This domain covers monitoring quality and performance, troubleshooting failures, automating deployment, and controlling cost over time.

Data Security & Governance

Smallest domain by weight, but it protects everything the other three build. Authentication, authorization, encryption, privacy, logging, sovereignty, and access patterns all live here.

How to Think Like a Data Engineer

Where does the data originate? How fast does it arrive? How does the schema change over time, and where does validation or enrichment actually belong in the flow?

Choose a store from access pattern, structure, lifecycle, durability, performance, and cost — never from a generic “best database” label someone repeated to you.

Plan for dependencies, retries, idempotency, alerts, logging, repeatable deployment, and a real recovery path if a job fails halfway through.

Make data discoverable, understandable, authorized, traceable, and actually fit for its intended use. A job that completes successfully is not the same thing as a high-quality dataset.

Protect data in storage and in transit, limit access to what’s actually needed, manage keys and policies deliberately, and know where data is allowed to live or move.

Volume, variety, velocity, partitioning, file formats, query patterns, compute duration, data movement, and storage lifecycle are all connected — pulling one lever moves the others.

DEA-C01 Exam Topics & AWS Data Services

This service list is a study map, not an exhaustive guarantee. The official in-scope page is non-exhaustive and subject to change.

Ingestion & streaming

Transformation & orchestration

Stores & analytics

Catalogs & governance

Security & quality

Current revision concepts

DEA-C01 Study & Practice Resources

Use practice to test whether you can actually trace data through a pipeline, choose a store from access patterns, and explain how security and governance shape the design — not just recall service names.

25 Free Practice Questions

Test your grasp of ingestion, transformation, storage, operations, quality, security, and governance before you commit study hours in the wrong direction.

DEA-C01 Premium Practice Package

Reinforce service choices and pipeline decisions with targeted practice and explanations that walk through the reasoning, not just the answer key.

DEA-C01 Study Guide

Review data-engineering concepts, service relationships, operational practices, and the revision-aware topics that older courses may have missed entirely.

DEA-C01 Cheat Sheet

Refresh pipeline patterns, service differences, security controls, and governance vocabulary once you’ve already done the deeper learning.

How I'd Have You Prepare for DEA-C01

Where candidates lose points

• Treating every data workload as a streaming problem because streaming feels more advanced.
• Choosing a data store before writing down the actual access pattern.
• Learning ETL tools while skipping quality, lineage, metadata, and governance entirely.
• Skipping SQL, programming concepts, version control, and data-lake fundamentals.
• Missing new revision topics because an older course was treated as if it were still complete.

DEA-C01 readiness checklist

• I can explain all four official domains and their weights.
• I can trace data through an ingestion-to-analytics pipeline step by step.
• I can compare batch and streaming based on stated requirements.
• I can choose a store from access patterns, lifecycle, and cost together.
• I can explain how quality, catalog, lineage, and governance actually differ.
• I can identify the security and operational controls that belong at each pipeline stage.

Test your data-engineering reasoning with a focused diagnostic.