- Exam Facts at a Glance
- Scoring Rules Worth Memorizing
- 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 the Guide Leaves Unresolved
- Scheduling the Domains
- Validity, Renewal, and Test-Day Logistics
- FAQ
- The exam has 65 questions (50 scored, 15 unscored) in 130 minutes, and the fee is USD 150.
- Passing requires a scaled score of 720 on a 100-1,000 scale, not 72% raw correct.
- Data Ingestion and Transformation carries 34% of scored content, the largest of the four domains.
- Guessing carries no penalty: unanswered items count as incorrect, so answer every question.
Exam Facts at a Glance
This page condenses the AWS Certified Data Engineer - Associate (DEA-C01) into the facts most worth memorizing before test day. Everything here applies to this credential only, and the details come from the AWS credential page and the DEA-C01 Exam Guide v1.1, published December 12, 2025, which is the latest revision listed in the revision record we reviewed. For a deeper walkthrough of how to build a preparation plan around these facts, see our DEA-C01 study guide.
| Item | What to Know |
|---|---|
| Issuer | AWS |
| Delivery | Pearson VUE, at a test center or via online proctoring |
| Fee | USD 150, plus applicable taxes under AWS testing policies |
| Questions | 65 total: 50 scored and 15 unscored (unscored items are not identified) |
| Time | 130 minutes |
| Item types | Multiple-choice and multiple-response |
| Passing score | Minimum scaled score of 720 on a 100-1,000 scale |
| Languages | English, Japanese, Korean, Simplified Chinese |
| Validity | Three years |
| Prerequisites | None required under AWS eligibility policy |
The two item types behave differently. A multiple-choice item has one correct answer and three distractors. A multiple-response item has two or more correct responses among five or more options. Read the stem carefully for how many selections it asks for before you start eliminating options.
For the money side, including how the 50% next-exam discount benefit works for holders of an active AWS Certification, see the DEA-C01 certification cost breakdown. That benefit is a discount on a next certification exam, not an unrestricted member discount and not a guaranteed price for every candidate. No member/non-member price schedule is published for this credential.
Scoring Rules Worth Memorizing
Several scoring facts get misquoted in forums. Keep these straight:
- 720 is a scaled score, not a percentage. You cannot convert a practice-test percentage into the official scaled score, so treat any "you need 72%" claim as a misreading. Our DEA-C01 passing score guide goes deeper.
- Scoring is compensatory. There is no separate pass requirement per domain. A weak showing in one area can be offset by strength elsewhere, though the weighting means ignoring the 34% domain is risky.
- Unanswered items are incorrect, and there is no guessing penalty. Never leave a question blank.
- 15 of the 65 questions are unscored and unidentified, so treat every question as if it counts.
- No issuer pass rate was found in the sources we reviewed. Be skeptical of any quoted figure; our DEA-C01 pass rate analysis explains what can and cannot be said.
Domain 1: Data Ingestion and Transformation (34%)
This is the heaviest domain and holds four tasks: perform data ingestion (1.1), transform and process data (1.2), orchestrate data pipelines (1.3), and apply programming concepts (1.4). The AWS guide notes its content list is not exhaustive, but these are the recurring themes.
Task 1.1: Ingestion
Know how to read from streaming and batch sources and how to trigger and throttle those reads.
- Streaming sources: Kinesis, Amazon MSK, DynamoDB Streams, DMS, Glue, Redshift.
- Batch sources: S3, Glue, EMR, DMS, Redshift, Lambda, AppFlow.
- Schedulers and triggers: EventBridge, Apache Airflow (MWAA), time-based jobs and crawlers, S3 Event Notifications, and invoking Lambda through Kinesis.
- Throttling and rate limits: expect scenarios involving DynamoDB, RDS, and Kinesis.
- Concepts: stream fan-in and fan-out, replayability of ingestion pipelines, and stateful versus stateless transactions.
- Connectivity: connecting sources that require IP allowlists.
Task 1.2: Transformation and Processing
Choose the right engine and format for the job, then debug it.
- Transform with EMR, Glue, Lambda, or Redshift depending on requirements and cost.
- Convert formats, such as .csv to Apache Parquet, a classic cost and performance lever.
- Connect sources via JDBC and ODBC, and integrate multiple sources.
- Optimize container performance on EKS and ECS.
- Define data volume, velocity, and variety, and build data APIs for other systems.
- Integrate LLMs into processing (a data-engineering skill, not model training or inference).
Task 1.3: Orchestration
Know which orchestrator fits which workflow.
- Options named in the guide: Lambda, EventBridge, MWAA, Step Functions, and Glue workflows.
- Design for performance, availability, scalability, resiliency, and fault tolerance.
- Implement serverless workflows and configure alerts with SNS and SQS.
Task 1.4: Programming Concepts
This is about engineering practice rather than language syntax; language-specific syntax is explicitly out of scope.
- Languages named in the guide: Python, SQL, Scala, R, Java, Bash, PowerShell.
- Version control, testing, logging, and monitoring.
- Infrastructure as code with CloudFormation and CDK, and packaging serverless pipelines with SAM (Lambda, Step Functions, DynamoDB tables).
- Lambda concurrency and performance, plus mounting and using volumes within Lambda.
- CI/CD for data pipelines, distributed computing, and data structures such as graphs and trees.
Because this domain is the largest, it deserves the most practice volume. The DEA-C01 exam domains guide maps all four content areas in more depth.
Domain 2: Data Store Management (26%)
Four tasks: choose a data store (2.1), understand data cataloging systems (2.2), manage the lifecycle of data (2.3), and design data models and schema evolution (2.4).
Task 2.1: Choosing a Data Store
- Match cost and performance needs to Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis Data Streams, or MSK, and configure for access patterns.
- Use-case matching examples from the guide: HNSW indexing in Aurora PostgreSQL, and fast key/value access in MemoryDB.
- Know the vector index types HNSW and IVF.
- Redshift federated queries, materialized views, and Spectrum for remote querying.
- Locking behavior in Redshift and RDS, the Apache Iceberg open table format, and Transfer Family for migration.
Task 2.2: Cataloging
- Glue Data Catalog and Hive metastore as technical catalogs.
- Glue crawlers for schema discovery and catalog population, plus partition synchronization.
- SageMaker Catalog for business catalogs.
Task 2.3: Lifecycle
- Loading and unloading between S3 and Redshift.
- S3 Lifecycle for tier changes and expiration; S3 versioning and DynamoDB TTL.
- Deleting data for business or legal requirements, and protecting data for resilience and availability.
Task 2.4: Models and Schema Evolution
- Schema design for Redshift, DynamoDB, and Lake Formation, and adapting to changing data characteristics.
- Indexing, partitioning, and compression best practices.
- Lineage with SageMaker ML Lineage Tracking and SageMaker Catalog.
- Vectorization concepts, including the Amazon Bedrock knowledge base.
Domain 3: Data Operations and Support (22%)
Four tasks: automate data processing (3.1), analyze data (3.2), maintain and monitor pipelines (3.3), and ensure data quality (3.4).
Automation and Analysis (Tasks 3.1 and 3.2)
- Orchestrate with MWAA and Step Functions, troubleshoot managed workflows, and use SDKs to access AWS features programmatically.
- Process with EMR, Redshift, and Glue; prepare transformations with Glue DataBrew and SageMaker Unified Studio; query with Athena.
- Visualize with DataBrew and QuickSight; verify and clean with Lambda, Athena, QuickSight, Jupyter Notebooks, and SageMaker Data Wrangler.
- Write SQL queries and views in Redshift and Athena, explore with Athena Spark notebooks, and compare provisioned versus serverless tradeoffs.
- Define aggregation, rolling averages, grouping, and pivoting.
Monitoring and Quality (Tasks 3.3 and 3.4)
- CloudWatch Logs for application logging, CloudTrail for API tracking, and log analysis with Athena, EMR, OpenSearch Service, and CloudWatch Logs Insights.
- Troubleshooting Glue and EMR pipelines, configuring monitoring alerts, and extracting audit logs.
- Quality checks during processing (such as empty fields), DataBrew quality rules and consistency checks, data-sampling techniques, and handling data skew.
Domain 4: Data Security and Governance (18%)
The smallest domain still has five tasks: authentication (4.1), authorization (4.2), encryption and masking (4.3), audit logs (4.4), and privacy and governance (4.5). Because scoring is compensatory, strong security knowledge can rescue a shaky domain elsewhere, and vice versa.
| Task | Anchor Services and Concepts |
|---|---|
| 4.1 Authentication | VPC security groups, IAM roles and endpoints, Secrets Manager credential rotation, S3 Access Points, PrivateLink, SageMaker Unified Studio domains, domain units, and projects |
| 4.2 Authorization | Custom least-privilege IAM policies, Secrets Manager and Parameter Store, Redshift user/group/role authorization, Lake Formation permissions, role-/tag-/attribute-based access |
| 4.3 Encryption and masking | KMS keys, cross-account encryption, encryption in and before transit, masking and anonymization for legal or policy needs |
| 4.4 Audit logs | CloudTrail, CloudTrail Lake, CloudWatch Logs, Athena and OpenSearch Service for analysis, large-volume EMR logs |
| 4.5 Privacy and governance | Redshift sharing permissions, Macie and Lake Formation for PII, Region restrictions on replication and backups, AWS Config, data sovereignty, SageMaker Catalog access control |
Scope Quirks the Guide Leaves Unresolved
A few inconsistencies in the published materials are worth knowing so they do not rattle you on exam day:
- AWS SCT. 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 DMS Schema Conversion. Study DMS Schema Conversion well; treat SCT as low-priority but not impossible until AWS clarifies.
- Amazon 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 text; do not assume anything beyond that.
- Duplicate listing. Amazon S3 Tables appears twice in the storage inventory. It is a duplicated line, not a double objective.
- Not exhaustive. Both the content list and the service lists are non-exhaustive and subject to change.
On the other side of the line, the guide lists services that are out of scope, such as AWS Amplify, AWS AppSync, AWS X-Ray, Amazon FinSpace, and the AWS IoT family. Machine-learning model training and inference, programming-language-specific syntax, and drawing business conclusions from data are explicitly outside the job tasks tested.
Scheduling the Domains
If you have a few weeks, sequence by weight and dependency rather than by service alphabet. This is one suggested order for DEA-C01 specifically:
Domain 1 first
- It is 34% of scored content and its streaming, batch, and orchestration concepts underpin the other domains.
- Build small pipelines with Kinesis, Glue, and Step Functions rather than only reading about them.
Domain 2
- Compare Redshift, DynamoDB, RDS, and S3-based lakes on access patterns, then cover the Glue Data Catalog and S3 Lifecycle.
- Add Iceberg and vector index concepts here.
Domains 3 and 4
- Monitoring, data quality, IAM, Lake Formation, KMS, and auditing share many services, so they reinforce each other.
- Finish with timed mixed sets to rehearse the 130-minute pace.
Not sure how much time you need? Our DEA-C01 difficulty guide helps calibrate. Once you have covered the content, test yourself with realistic sets on the DEA-C01 practice test site, keeping in mind that practice percentages cannot be converted into the official scaled score.
Validity, Renewal, and Test-Day Logistics
Renewal
The certification is valid for three years. You can renew by passing the latest version of the DEA-C01 exam, and the AWS recertification table also lists passing AWS Certified Generative AI Developer - Professional as a route to renew an active Data Engineer Associate certification for three years. The new three-year period runs from the date you complete the renewal action, not from the previous expiry date. The Data Engineer Associate row does not list a CEU/CPE quota or a Skill Builder maintenance route, so do not borrow options from other certifications.
Eligibility
No prior certification, degree, mandatory course, or work-hour total is required. The recommended profile of two to three years in data engineering or data architecture and one to two years of AWS experience is guidance, not an admission rule. The general minimum age is 13, with parent or guardian consent required for ages 13-17. See DEA-C01 requirements for the full picture.
Accommodations and Conditions
An eligible ESL +30-minute accommodation must be requested before booking, and it is not silently added to the standard timer. Online candidates cannot leave camera view for an unapproved break. Details on calculators, books, and adaptive testing were not established in the passages we reviewed, so confirm them in the Pearson VUE and AWS testing policies before exam day rather than assuming.
Key Takeaway
Verify the exam guide version and the in-scope service list on the AWS site the week you book. The guide is non-exhaustive and the service lists can change, so a short recheck beats relying on any cached summary, including this one.
Career Context
The certification targets roles that build and maintain ETL pipelines, data lakes, and warehouses on AWS. No verified credential-specific salary premium or issuer pass rate was found, so treat any such headline with caution. For a sober look, read the DEA-C01 salary guide and the DEA-C01 ROI analysis.
FAQ
There are 65 questions in total: 50 scored and 15 unscored. The unscored items are not identified, so answer every question carefully. You have 130 minutes.
No. The requirement is a minimum scaled score of 720 on a 100-1,000 scale. This is not a raw-percentage threshold, and practice-test percentages cannot be converted into the official scaled score.
Data Ingestion and Transformation at 34%. The others are Data Store Management at 26%, Data Operations and Support at 22%, and Data Security and Governance at 18%.
No. Scoring is compensatory across the whole exam rather than requiring a separate pass in each domain, and there is no penalty for guessing, though unanswered items count as incorrect.
It is valid for three years. Renew by passing the latest version of the exam or, per the AWS recertification table, by passing AWS Certified Generative AI Developer - Professional. The new period starts when you complete the renewal action.