- DEA-C01 is the AWS Certified Data Engineer - Associate exam: 65 questions, 130 minutes, USD 150, scaled passing score of 720.
- Data Ingestion and Transformation is the heaviest domain at 34%, ahead of Data Store Management at 26%.
- The 720 passing mark is a scaled score on a 100-1,000 range, not a 72% raw percentage.
- Two to three years of data engineering experience is recommended guidance, not an admission requirement.
What DEA-C01 Actually Is
DEA-C01 is the exam code for the AWS Certified Data Engineer - Associate certification, issued by AWS. If you landed here after seeing the code in a job posting or a training catalog, this is the credential in question: an associate-level exam that tests whether you can choose, build, secure and operate data pipelines and data stores on AWS.
The acronym is also used by other, unrelated credentials elsewhere, so it helps to anchor on the full name. Everything on this page refers only to the AWS certification. If you want a deeper treatment of the naming itself, see What Does DEA-C01 Stand For? and What Is DEA-C01 Certification?.
The exam covers the full life of a data pipeline: pulling data in from streams, databases and files; transforming and orchestrating it; storing it in the right engine with the right catalog and lifecycle; keeping it running and trustworthy; and locking it down with authentication, authorization, encryption and audit logging. It is a knowledge exam, so passing it shows you understand these topics, not that you have proven hands-on competence.
Who the Credential Targets
The exam guide describes a target candidate with roughly two to three years of experience in data engineering or data architecture and one to two years of hands-on AWS experience. That is a recommendation, and AWS does not enforce it. Our DEA-C01 requirements guide covers the eligibility rules in full.
The guide's picture of the ideal candidate is useful because it tells you what background the questions assume:
- General IT knowledge: setting up and maintaining ETL pipelines, applying programming concepts independent of any single language, using Git and source control, understanding data lakes, and core networking, storage and compute concepts, plus vector concepts.
- AWS knowledge: choosing services based on cost, performance and functionality; writing SQL; transforming data; checking data quality and consistency; and securing data.
The guide also lists what is explicitly out of scope as job tasks: machine learning model training and inference, programming-language-specific syntax, and drawing business conclusions from data. Even though Python, SQL, Scala, R, Java, Bash and PowerShell appear in the skills list, you are not being tested on syntax trivia. You are being tested on engineering judgment.
Typical roles that map to this credential include data engineer, analytics engineer, ETL developer and cloud data architect. For a sober look at employer demand, see DEA-C01 jobs. For earnings context, the DEA-C01 salary guide explains why no credential-specific salary premium should be assumed.
Exam Format at a Glance
| Attribute | DEA-C01 Detail |
|---|---|
| Full name | AWS Certified Data Engineer - Associate (DEA-C01) |
| Questions | 65 total: 50 scored and 15 unscored (unscored items are not identified) |
| Time | 130 minutes |
| Question types | Multiple-choice and multiple-response |
| Passing score | Scaled score of 720 on a 100-1,000 scale |
| Fee | USD 150, plus applicable taxes |
| Delivery | Pearson VUE, at a test center or via online proctoring |
| Languages | English, Japanese, Korean, Simplified Chinese |
| Validity | Three years |
| Current guide | DEA-C01 Exam Guide v1.1, published December 12, 2025 |
How the questions are built
A multiple-choice item has one correct answer and three distractors. A multiple-response item has two or more correct answers among five or more options. Because 15 of the 65 questions are unscored and indistinguishable from the rest, treat every question as if it counts.
There are no scheduled breaks. At a test center, an unscheduled break still consumes exam time, and online candidates cannot leave camera view without approval. An eligible ESL accommodation adds 30 minutes, but it must be requested before booking.
The Four Content Domains
The exam objectives are organized into four domains covering 17 task statements and 120 numbered skills. The weights apply to scored content. For a task-by-task breakdown, the DEA-C01 exam domains guide goes deeper than this overview.
Domain 1: Data Ingestion and Transformation (34%)
The largest domain, with four tasks: perform data ingestion, transform and process data, orchestrate data pipelines, and apply programming concepts.
- Streaming reads from Kinesis, MSK, DynamoDB Streams and DMS versus batch reads from S3, Glue, EMR, Redshift, Lambda and AppFlow.
- Schedulers and event triggers (EventBridge, Airflow, S3 Event Notifications), plus Lambda invoked through Kinesis.
- Throttling and rate limits, stream fan-in and fan-out, and the replayability of ingestion pipelines.
- Format conversion such as .csv to Apache Parquet, debugging transformation failures, and integrating LLMs into processing.
- Orchestration with Step Functions, MWAA, Glue workflows and EventBridge; alerts through SNS and SQS.
- Infrastructure as code with CloudFormation, CDK and SAM, Lambda concurrency tuning, and CI/CD concepts.
Domain 2: Data Store Management (26%)
Four tasks: choose a data store, understand data cataloging systems, manage the lifecycle of data, and design data models and schema evolution.
- Matching workloads to Redshift, DynamoDB, RDS, EMR, Lake Formation, Kinesis Data Streams or MSK on cost and performance.
- Specialized fits such as HNSW indexing in Aurora PostgreSQL, fast key/value access in MemoryDB, and vector index types (HNSW, IVF).
- Redshift federated queries, materialized views and Spectrum; open table formats such as Apache Iceberg.
- Glue Data Catalog, crawlers, partition synchronization, and business catalogs through SageMaker Catalog.
- S3 Lifecycle tiering and expiry, versioning, DynamoDB TTL, and deletion for legal or business reasons.
- Schema design, schema conversion, lineage tracking, and partitioning, indexing and compression practices.
Domain 3: Data Operations and Support (22%)
Four tasks: automate data processing, analyze data, maintain and monitor pipelines, and ensure data quality.
- Automating with Lambda, EventBridge, MWAA and Step Functions, and troubleshooting managed workflows.
- Querying with Athena, building views in Athena and Redshift, and comparing provisioned versus serverless tradeoffs.
- Visualization and cleaning with DataBrew, QuickSight and SageMaker Data Wrangler.
- Monitoring with CloudWatch Logs, CloudTrail and log analysis in Athena or OpenSearch Service.
- Data quality rules in DataBrew, empty-field checks, sampling techniques and handling data skew.
Domain 4: Data Security and Governance (18%)
Five tasks: authentication, authorization, encryption and masking, audit-ready logging, and data privacy and governance.
- Security groups, IAM roles and policies, Secrets Manager credential rotation, S3 Access Points and PrivateLink.
- Custom least-privilege policies, Lake Formation permissions, and role-, tag- and attribute-based authorization.
- KMS encryption, cross-account encryption, and masking or anonymization for legal and policy needs.
- CloudTrail Lake, CloudWatch Logs and Athena for audit trails.
- Macie and Lake Formation for PII, Config for configuration changes, Regional restrictions and data sovereignty.
Key Takeaway
Ingestion, transformation and orchestration together carry a third of the scored content, so pipeline design decisions (batch versus streaming, replay, throttling, orchestration choice) deserve your most repeated practice.
Services You Will Meet
AWS publishes an in-scope service list, and it is broad. It is also non-exhaustive and subject to change, so check it again before test day. Grouped by theme, the heavy hitters include:
- Analytics: Athena, EMR, Glue, Glue DataBrew, Lake Formation, Kinesis Data Streams, Kinesis Data Firehose, Managed Service for Apache Flink, MSK, OpenSearch Service, Amazon Quick and SageMaker AI.
- Application integration: AppFlow, EventBridge, MWAA, SNS, SQS and Step Functions.
- Databases: DynamoDB, RDS, Aurora, Redshift, MemoryDB, DocumentDB, Keyspaces and Neptune.
- Compute and containers: Lambda, EC2, Batch, SAM, ECS, EKS and ECR.
- Storage and transfer: S3, S3 Tables, S3 Glacier, EBS, EFS, AWS Backup, DMS, DataSync, Snow Family and Transfer Family.
- Security and governance: IAM, KMS, Macie, Secrets Manager, CloudTrail, CloudWatch, Config and Systems Manager.
- Developer tools and ML: CLI, CloudFormation, CDK, CodeBuild, CodeDeploy, CodePipeline, Bedrock and Kendra.
Some services are explicitly out of scope, such as Amazon FinSpace, AWS AppSync, AWS Amplify, the AWS IoT family and the Elemental media services. If a service appears on the out-of-scope list, you do not need to study it for this exam.
Scope Quirks Worth Knowing
The published materials contain a few inconsistencies that candidates should be aware of rather than assume away.
- Quick versus QuickSight: The service inventory says Amazon Quick, while Skills 3.2.1 and 3.2.2 say QuickSight. Both labels appear in official materials, so recognize both names.
- Duplicate S3 Tables entry: Amazon S3 Tables appears twice in the storage inventory. That is a listing quirk, not an extra objective.
- Newer topics: LLM integration in data processing, vector indexes, Apache Iceberg, SageMaker Catalog and SageMaker Unified Studio domains and projects are all in the skills list. Study them as data-engineering topics, not as machine learning model training.
Registration, Fees and Eligibility
You schedule through Pearson VUE and choose either a test center or online proctoring. The exam fee is USD 150 plus any taxes that apply under AWS testing policies. AWS does not publish a member versus non-member price schedule for this credential. An active AWS Certification can give you a 50% discount benefit toward a next certification exam, but that is a specific benefit with conditions, not a universal discount. Our certification cost breakdown walks through the full accounting, and exam dates and scheduling covers booking logistics.
There is no required prior certification, degree, mandatory course or minimum work-hour total. The general minimum age is 13, and candidates aged 13 to 17 need parent or guardian consent under AWS policy. If you need the ESL +30-minute accommodation, request it before you book.
Validity and Renewal
The certification is valid for three years. The credential page describes renewal by passing the latest version of the DEA-C01 exam. AWS's dedicated recertification table also lists passing AWS Certified Generative AI Developer - Professional as a way to renew an active Data Engineer Associate certification for three years. The renewed period runs from the date you complete the recertification action, not from your previous expiry date. The Data Engineer Associate row does not list a CEU/CPE quota or a Skill Builder maintenance route, so do not assume options shown for other certifications apply here.
Sequencing Your Preparation
Rather than a generic plan, let the exam's weighting drive the order. A reasonable DEA-C01 sequence looks like this, and you can compress or stretch it to fit your background. The DEA-C01 study guide expands each phase.
Ingestion, transformation and orchestration
- Start here because Domain 1 is 34% of scored content.
- Contrast Kinesis, MSK and DMS streaming with batch paths; practice replay, throttling and fan-out scenarios.
- Compare Step Functions, MWAA and Glue workflows for orchestration.
Data stores and cataloging
- Match workloads to Redshift, DynamoDB, RDS and S3-based lakes.
- Learn Glue crawlers, the Data Catalog, S3 Lifecycle and Iceberg basics.
Operations and quality
- Cover Athena, CloudWatch, CloudTrail, DataBrew quality rules and data skew.
Security and governance, then review
- Work through IAM, Lake Formation permissions, KMS, Macie and audit logging.
- Finish with timed mixed-domain practice and the DEA-C01 cheat sheet.
If you want to gauge readiness against the exam style, the DEA-C01 practice tests mirror the single-answer and multiple-response formats. Remember that practice percentages are only a rough signal, since the real score is scaled. For perspective on effort, read how hard the DEA-C01 exam is, and for pass-rate expectations see the pass rate discussion, which explains that no issuer pass-rate statistic has been published.
Frequently Asked Questions
DEA-C01 is the exam code for the AWS Certified Data Engineer - Associate certification. It identifies the exam, which validates skills in building and managing data pipelines, data stores, operations and security on AWS.
There are 65 questions in 130 minutes. Fifty are scored and fifteen are unscored, and you cannot tell which is which. Questions are multiple-choice or multiple-response.
No. The passing score is a scaled 720 on a 100-1,000 scale. It is not a 72% raw score, and practice-test percentages cannot be converted into the official scaled result.
No. AWS requires no prior certification, degree or mandatory course. The two to three years of data engineering and one to two years of AWS experience is recommended guidance only.
It is valid for three years. You can renew by passing the latest version of the exam, and AWS's recertification table also lists AWS Certified Generative AI Developer - Professional as a renewal route. The new three-year period starts when you complete the renewal action.
For related background, you can also explore the DEA-C01 certification overview and whether the credential is worth the investment.