ETL: Extract, transform, and load workflow for moving and reshaping data into a target system.
CDC: Change data capture, where inserts, updates, and deletes are emitted as change events for downstream processing.
Data lake: Centralized storage pattern that keeps raw and curated data available for many analytics tools and downstream consumers.
What is AWS Certified Data Engineer — Associate (DEA-C01)?
DEA-C01 is an associate-level AWS certification focused on building and operating data pipelines and analytics platforms on AWS: ingestion, transformation, storage, monitoring/troubleshooting, and security/governance.
If you want the fastest orientation, start with the section overview and keep the official exam guide from Resources open while you study.
What score do you need to pass DEA-C01?
AWS uses a scaled score (100–1000). The minimum passing score is 720.
How many questions and how much time?
- 65 questions
- 130 minutes
- Multiple-choice and multiple-response
Who should take DEA-C01?
AWS describes the ideal candidate as having:
- ~2–3 years of experience in data engineering or data architecture
- At least 1–2 years of hands-on AWS experience
AWS also expects the candidate to be comfortable with:
- SQL on AWS services
- general networking, storage, and compute concepts
- data-lake and ETL pipeline concepts
- high-level programming ideas without testing language-specific syntax
What is in scope versus out of scope?
The current AWS Documentation guide makes this clearer than many older summaries:
- In scope: data ingestion, transformation, store selection, schema/catalog work, monitoring, troubleshooting, IAM/Lake Formation, encryption, privacy, and governance
- Out of scope: ML training and inference, language-specific programming syntax, and drawing business conclusions from data
That means DEA-C01 is still a data-platform and pipeline exam, not a data science modeling exam.
What makes DEA-C01 hard?
Most misses come from weak trade-off reading, not from forgetting one service name. The exam is hardest when candidates blur together:
- ingestion pattern versus transformation engine
- lake storage versus serving store versus warehouse
- query engine versus dashboard tool versus notebook workflow
- encryption versus authorization versus governance
If you keep confusing those boundaries, stay in the Cheat Sheet and weak domain lessons before doing more timed sets.
What are the current domain weights?
The current AWS Documentation guide weights DEA-C01 this way:
- Domain 1: Data Ingestion and Transformation — 34%
- Domain 2: Data Store Management — 26%
- Domain 3: Data Operations and Support — 22%
- Domain 4: Data Security and Governance — 18%
If you are short on time, bias your review in that order.
What AWS services should you know?
At a high level, you should be comfortable with:
- Data lake and governance: Amazon S3, AWS Lake Formation, AWS Glue Data Catalog
- ETL and processing: AWS Glue, Amazon EMR (Spark), AWS Lambda, AWS Glue DataBrew
- Streaming and ingestion: Amazon Kinesis, Amazon MSK, AWS DMS, Amazon AppFlow
- Analytics: Amazon Athena, Amazon Redshift, Amazon QuickSight
- Orchestration: Amazon EventBridge, AWS Step Functions, Amazon MWAA
- Ops and security: CloudWatch, CloudTrail, IAM, KMS, Macie
Use the Cheat Sheet for service pickers and high-yield patterns.
Do you need deep coding knowledge?
No. DEA-C01 does not test language-specific syntax. But AWS still expects you to reason about:
- SQL structure and query behavior
- Lambda and SDK usage at a practical level
- IaC for repeatable deployment
- runtime and scan-efficiency basics
If you can explain what the code or template should accomplish, you are usually in the right depth range.
How long should you study for DEA-C01?
Typical ranges vary by hands-on experience:
- Strong AWS + strong data background: 40–60 hours
- Strong data background but newer to AWS: 60–90 hours
- Newer to data engineering platforms: 90–120+ hours
Pick a schedule you can sustain, then cycle between the Cheat Sheet and Resources so you keep service choice and official scope aligned.
What is the minimum useful hands-on lab set?
If your time is tight, the smallest useful practical loop is:
- Load and query files in S3 with Athena
- Review one Glue ETL or transformation path
- Compare Athena, Redshift, and one serving-store decision
- Walk one EventBridge plus Step Functions or MWAA orchestration path
- Review IAM, Lake Formation, KMS, CloudTrail, and Config together
That is enough to stop answering from buzzwords alone.
How do you practice effectively for DEA-C01?
Follow a loop:
- Read one objective area from the official exam guide in Resources
- Review the matching service patterns in the Cheat Sheet
- Write 3–5 “miss rules” from what you got wrong
- Re-drill weak tasks 48–72 hours later (spaced repetition)
What should you do in the last week?
Use a narrow loop:
- Day 7 to Day 5: re-read the Cheat Sheet and weak domain lessons
- Day 4 to Day 3: one or two timed mixed sets
- Day 2: review only misses, especially store fit, orchestration, and governance tie-breaks
- Day 1: light review only, mostly Glossary and your own miss rules
If you still confuse Athena vs Redshift, Glue vs EMR, or Lake Formation vs IAM, you are not ready to coast yet.
Which official guide should you trust?
Trust the current AWS Documentation exam guide from Resources first. AWS also publishes a revisions page there, which is the safest place to check whether an older blog post or PDF summary has drifted.