- What the DEA-C01 Certification Validates
- Exam Mechanics: Format, Fee, and Scoring
- 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%)
- Scope Quirks You Should Know About
- Who Should Sit This Exam
- Sequencing Your Preparation by Domain Weight
- Validity and Renewal
- Frequently Asked Questions
- The exam has 65 questions (50 scored, 15 unscored) in 130 minutes, costing USD 150 plus applicable taxes.
- You need a scaled score of 720 on a 100-1,000 scale; this is not a 72% raw-score threshold.
- Data Ingestion and Transformation carries 34% of scored content, the heaviest of four domains.
- No prerequisite certification or degree is required; two to three years of experience is guidance, not a gate.
What the DEA-C01 Certification Validates
The AWS Certified Data Engineer - Associate (DEA-C01) credential is issued by AWS and targets people who build and maintain data pipelines on AWS. It is not a general cloud exam. It tests whether you can pick the right ingestion pattern, transform data efficiently, choose an appropriate data store, operate pipelines in production, and keep data secured and governed.
If you are new to the acronym, our explainers on what DEA-C01 is and what DEA-C01 stands for cover the naming in more detail. Here the focus is on what the certification actually demands, because the official exam guide is organized into four content domains, 17 task statements, and 120 numbered skills.
The guide's target candidate profile is worth reading closely. It describes general IT experience with ETL pipelines, source control such as Git, data lakes, and core networking, storage, and compute concepts, plus AWS experience in choosing services on cost, performance, and functionality. The same profile lists SQL, data transformation, data-quality and consistency work, and data security. AWS also explicitly places machine-learning model training and inference, language-specific syntax, and drawing business conclusions from data outside the exam's job-task scope.
Exam Mechanics: Format, Fee, and Scoring
Knowing the logistics removes avoidable surprises on test day. The facts below come from the AWS credential page, exam guide, and testing policies.
| Item | DEA-C01 Detail |
|---|---|
| Governing body | AWS |
| Delivery | Pearson VUE, at a test center or via online proctoring |
| Fee | USD 150, with applicable taxes |
| Questions | 65 total: 50 scored, 15 unscored (not identified to you) |
| Time | 130 minutes |
| Question types | Multiple-choice and multiple-response |
| Passing score | Minimum scaled score of 720 (scale 100-1,000) |
| Languages | English, Japanese, Korean, Simplified Chinese |
| Validity | Three years |
Question styles
The guide describes two item types. 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 unanswered items count as incorrect and there is no penalty for guessing, you should answer every question. Fifteen of the 65 items are unscored pilot questions, and you cannot tell which ones, so treat every item as if it counts.
What 720 really means
The passing score is a scaled score, not a percentage. A practice-test percentage cannot be converted into the official scaled score, so be skeptical of any provider that promises "you need X% to pass." Scoring is compensatory across the whole exam: you do not need to pass each domain separately, and strength in one area can offset weakness in another. For a deeper treatment, see our guide to the DEA-C01 passing score. If you want a realistic read on difficulty, the DEA-C01 difficulty guide weighs the evidence.
Pricing and discounts
The exam fee is USD 150 plus applicable taxes. AWS does not publish a member versus non-member price schedule for this credential. An active AWS Certification provides a 50% discount benefit toward a next certification exam, but this is a restricted benefit, not an unconditional discount available to every candidate. Our DEA-C01 certification cost breakdown walks through the full budget, and the exam dates and scheduling guide explains booking.
Test-day conditions
- There are no scheduled breaks. At a test center, unscheduled breaks consume exam time; online candidates cannot leave camera view for an unapproved break.
- An eligible ESL accommodation of 30 additional minutes must be requested before booking. It is not silently added to the standard timer.
- The general minimum candidate age is 13, with parent or guardian consent required for ages 13-17.
Domain 1: Data Ingestion and Transformation (34%)
This is the largest domain and deserves the largest share of your time. It spans four tasks: data ingestion, transformation and processing, pipeline orchestration, and programming concepts. For the complete four-domain picture, see our DEA-C01 exam domains guide.
Task 1.1: Perform data ingestion
You must distinguish streaming from batch and know which services suit each.
- Streaming reads from Kinesis, Amazon MSK, DynamoDB Streams, AWS DMS, AWS Glue, and Redshift.
- Batch reads from S3, Glue, EMR, DMS, Redshift, Lambda, and Amazon AppFlow.
- Scheduling with EventBridge, Apache Airflow, and time-based jobs or crawlers; event triggers with S3 Event Notifications and EventBridge.
- Handling throttling and rate limits for DynamoDB, RDS, and Kinesis.
- Stream fan-in and fan-out, replayability of ingestion pipelines, and the difference between stateful and stateless transactions.
Task 1.2: Transform and process data
Expect scenario questions about picking the right engine and fixing problems.
- Choosing among EMR, Glue, Lambda, and Redshift for a transformation requirement.
- Converting formats, such as CSV to Apache Parquet, for query efficiency.
- Connecting sources over JDBC and ODBC, and integrating multiple sources.
- Optimizing processing cost, debugging transformation failures and performance, and tuning EKS and ECS container performance.
- Building data APIs for other systems and integrating LLMs into processing, which stays at the data-engineering level rather than ML training or inference.
Task 1.3: Orchestrate data pipelines
Know when each orchestrator fits.
- Lambda, EventBridge, Amazon MWAA, Step Functions, and Glue workflows for ETL orchestration.
- Designing for performance, availability, scalability, resiliency, and fault tolerance.
- Serverless workflows and alerting through SNS and SQS.
Task 1.4: Apply programming concepts
This task is about engineering practice, not syntax.
- Infrastructure as code with CloudFormation and CDK; packaging serverless pipelines with AWS SAM.
- Lambda concurrency and performance tuning, including mounting volumes.
- Version control, testing, logging, monitoring, and CI/CD concepts.
- Distributed computing, plus data structures such as graphs and trees.
Domain 2: Data Store Management (26%)
This domain asks you to match storage to access patterns, catalog data, manage its lifecycle, and design schemas that evolve.
Choosing a data store (Task 2.1)
You will weigh Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis Data Streams, and MSK on cost and performance. The skill list also includes some newer-feeling topics: HNSW indexing in Aurora PostgreSQL, fast key/value access in MemoryDB, vector index types (HNSW and IVF), and open table formats such as Apache Iceberg. Other skills cover Redshift federated queries, materialized views, and Spectrum, locking behavior in Redshift and RDS, and AWS Transfer Family for migration.
Cataloging (Task 2.2)
Understand the AWS Glue Data Catalog and Hive metastore as technical catalogs, how Glue crawlers discover schema and populate catalogs, how partitions are synchronized, and how business catalogs are managed with SageMaker Catalog.
Lifecycle (Task 2.3)
Know loading and unloading between S3 and Redshift, S3 Lifecycle for tiering and expiration, S3 versioning, DynamoDB TTL, deleting data for business or legal reasons, and protecting data for resilience.
Modeling and schema evolution (Task 2.4)
This covers schema design for Redshift, DynamoDB, and Lake Formation, adapting to changing data characteristics, lineage tracking, indexing, partitioning, and compression best practices, and vectorization concepts tied to an Amazon Bedrock knowledge base.
Key Takeaway
For Domain 2, build a decision table: for each store (Redshift, DynamoDB, RDS, Aurora, MemoryDB, S3 with Iceberg), write down its access pattern, consistency and locking behavior, and cost profile. Scenario questions reward fast elimination of stores that do not fit.
Domain 3: Data Operations and Support (22%)
This domain covers running pipelines day to day: automating processing, analyzing data, monitoring, and ensuring quality.
- Automation (3.1): MWAA and Step Functions orchestration, troubleshooting managed workflows, SDK access, Athena queries, Glue DataBrew and SageMaker Unified Studio for preparing transformations, and EventBridge schedulers.
- Analysis (3.2): visualization with DataBrew and QuickSight, verification and cleaning with Lambda, Athena, Jupyter Notebooks, and SageMaker Data Wrangler, SQL views in Redshift and Athena, Athena Spark notebooks, and provisioned versus serverless tradeoffs. Concepts like aggregation, rolling averages, grouping, and pivoting also appear.
- Monitoring (3.3): audit logs, CloudWatch Logs automation, CloudTrail API tracking, alerts, performance troubleshooting for Glue and EMR, and log analysis with Athena, OpenSearch Service, and CloudWatch Logs Insights.
- Data quality (3.4): checking for issues such as empty fields, DataBrew quality rules, consistency investigation, sampling techniques, and data skew.
Domain 4: Data Security and Governance (18%)
Although it is the smallest domain, security questions are scenario-heavy, and the compensatory scoring model means missing them still costs you points.
Authentication and authorization (Tasks 4.1 and 4.2)
Know how access is granted and constrained.
- VPC security groups, IAM groups, roles, and endpoints, and S3 Access Points and PrivateLink policies.
- Secrets Manager for creating and rotating credentials; Systems Manager Parameter Store for storing application and database credentials.
- Custom IAM policies when managed policies fall short, with least-privilege design.
- Lake Formation permissions across Redshift, EMR, Athena, and S3; role-, tag-, and attribute-based authorization.
- SageMaker Unified Studio domains, domain units, and projects.
Encryption, masking, and audit (Tasks 4.3 and 4.4)
Protect and trace data.
- KMS encryption and decryption, cross-account encryption, and encryption in or before transit.
- Masking and anonymization to satisfy law or policy.
- CloudTrail, CloudTrail Lake, CloudWatch Logs, and log analysis through Athena, Logs Insights, and OpenSearch Service, including large-volume EMR logs.
Privacy and governance (Task 4.5)
Handle where data lives and who sees it.
- Redshift sharing permissions, PII identification with Macie and Lake Formation, and AWS Config for inspecting account-configuration changes.
- Preventing replication or backups in prohibited Regions and meeting data sovereignty requirements.
- SageMaker Catalog project data access and governance frameworks and data-sharing patterns.
Scope Quirks You Should Know About
A careful reading of the published materials turns up a few inconsistencies. Knowing about them helps you avoid wasted effort and misplaced confidence.
- AWS SCT ambiguity: The v1.1 revision record removes AWS Schema Conversion Tool from the in-scope service list, yet Skill 2.4.3 still names AWS SCT alongside AWS DMS Schema Conversion. Both observations are real, so learn DMS Schema Conversion well and treat SCT as a lower-confidence topic until AWS clarifies.
- Quick versus QuickSight: As noted above, both labels appear in the official materials.
- Non-exhaustive lists: The guide states its content is not exhaustive, and the service lists are subject to change. Treat them as strong guidance, not a guarantee of boundaries.
- Version: DEA-C01 Exam Guide v1.1, published December 12, 2025, is the latest revision in the retrieved record. No separate go-live date was established, so do not assume one.
- Out of scope: Services such as Amazon FinSpace, AWS Amplify, AWS AppSync, AWS X-Ray, and the IoT and media families are listed as out of scope, so you can deprioritize them.
Who Should Sit This Exam
The guide recommends two to three years of experience in data engineering or data architecture and one to two years of hands-on AWS use. That is a recommendation, not an admission rule. No prior certification, degree, mandatory course, or specific work-hour total is required. Our requirements and eligibility guide covers the policy details.
Typical roles that benefit include data engineers, analytics engineers, ETL developers, and cloud engineers moving into data work. If you are weighing it against the Solutions Architect Associate (SAA-C03), the distinction is focus: SAA-C03 is a broad architecture exam, whereas DEA-C01 goes deep on pipelines, stores, and governance for data. Neither contributes objectives to the other. For career outcomes, read the salary guide, the ROI analysis, and our look at DEA-C01 jobs. Be aware that no verified credential-specific salary premium or official pass rate was found, so beware of headlines that promise either. Our pass rate analysis explains what the data does and does not show.
Sequencing Your Preparation by Domain Weight
Because scoring is compensatory and Domain 1 is the heaviest, a weighted plan makes sense. The schedule below is one way to order topics so that early weeks build skills the later ones depend on. Adjust the pacing to your own background; see the DEA-C01 study guide for a fuller plan and the cheat sheet for a final review.
Domain 1: Ingestion and Transformation
- Build a streaming path (Kinesis or MSK) and a batch path (S3 to Glue to Parquet).
- Wire an orchestrator: Step Functions first, then MWAA and Glue workflows.
- Practice IaC with CloudFormation or CDK and SAM deployment.
Domain 2: Data Store Management
- Compare Redshift, DynamoDB, RDS, and Aurora by access pattern.
- Set up Glue crawlers, the Data Catalog, S3 Lifecycle rules, and an Iceberg table.
Domain 3: Operations and Support
- Query with Athena, set up CloudWatch alarms, and trace a failing Glue job.
- Define data-quality rules in DataBrew.
Domain 4: Security and Governance
- Configure Lake Formation permissions, KMS keys, and Secrets Manager rotation.
- Review CloudTrail, Macie, and Region-restriction patterns.
Integrated review
- Take timed mixed-domain sets in the exam's 130-minute window using our practice tests.
- Revisit weak tasks and ambiguous items such as SCT versus DMS Schema Conversion.
Key Takeaway
Hands-on labs outperform reading for this exam. Scenario questions describe constraints (latency, cost, replay, permissions) and ask for the best fit, which you learn by building the pipelines yourself. Use practice questions to find gaps, not to memorize answers, and remember that practice scores cannot be converted into the official scaled score.
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. The current AWS Recertification table also lets you renew an active Data Engineer Associate certification by passing AWS Certified Generative AI Developer - Professional. In either case, the renewed three-year 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 one-year Skill Builder maintenance route, so do not rely on renewal options that apply to other certifications.
Frequently Asked Questions
The exam has 65 questions, of which 50 are scored and 15 are unscored. You have 130 minutes. The unscored items are not identified, so answer all of them. There is no penalty for guessing and unanswered questions count as incorrect.
You need a minimum scaled score of 720 on a 100-1,000 scale. This is not a 72% raw-score threshold, and a practice-test percentage cannot be converted into the official scaled score. Scoring is compensatory across the whole exam rather than requiring a pass in each domain.
The fee is USD 150, with applicable taxes under AWS testing policies. No member or non-member price schedule is published for this credential. An active AWS Certification offers a 50% discount benefit on a next certification exam, though it is not a guaranteed price for every candidate.
No. AWS does not require a prior certification, degree, mandatory course, or set number of work hours. The two to three years of data engineering experience and one to two years on AWS are recommendations for the target candidate, not admission rules. The minimum age is 13, with guardian consent for ages 13-17.
Start with Data Ingestion and Transformation, which makes up 34% of scored content, then move to Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%). Because scoring is compensatory, strong performance in the heavier domains can offset weaker areas, but none should be ignored.
The certification lasts three years. You can renew by passing the latest version of the DEA-C01 exam, or by passing AWS Certified Generative AI Developer - Professional per the AWS Recertification table. The new three-year period starts when you complete the renewal action, not from the old expiry date.