- What a DEA-C01 Actually Is
- Why the Code Causes Confusion
- Exam Mechanics: Format, Fee, Timing, and Scoring
- The Four Content Domains
- Services You Will Meet in the Scope
- Who Should Sit the Exam
- Registration, Languages, and Accommodations
- Validity and Renewal
- DEA-C01 Compared With SAA-C03
- Sequencing Your Preparation by Domain
- Open Questions in the Published Scope
- FAQ
- DEA-C01 is the exam code for AWS Certified Data Engineer - Associate, issued by AWS and delivered through Pearson VUE.
- The exam has 65 questions (50 scored, 15 unscored) in 130 minutes, with a passing scaled score of 720 out of 1,000.
- Data Ingestion and Transformation carries 34% of scored content, making it the heaviest of the four domains.
- The fee is USD 150 plus applicable taxes, and the credential is valid for three years.
What a DEA-C01 Actually Is
A DEA-C01 is an exam, and more precisely the exam code for AWS Certified Data Engineer - Associate (DEA-C01). When someone says "I passed my DEA-C01," they mean they passed the AWS exam that validates the skills of a data engineer working on the AWS platform: ingesting data from streaming and batch sources, transforming and orchestrating it, choosing and managing data stores, operating pipelines, and securing and governing the result.
The credential is governed by AWS. Candidates sit the exam at a Pearson VUE test center or through online proctoring. The version covered here is DEA-C01 Exam Guide v1.1, published December 12, 2025, which is the latest revision listed in the AWS revision record. If you want the short-form definitions, the site also has companion explainers such as What Does DEA-C01 Stand For? and DEA-C01 Meaning, but this article goes further into what the exam contains and how it works.
Why the Code Causes Confusion
The string "DEA-C01" is not unique to one program on the internet, and searching it can surface credentials from entirely different organizations. Everything on this page refers only to the AWS Certified Data Engineer - Associate exam. Fees, dates, domain weights, and scoring rules from other programs that happen to share the acronym do not apply here, and you should not mix them into your planning. When you evaluate a practice resource, confirm that it names AWS and lists the four domains below. For the full certification overview, see DEA-C01 Certification.
Exam Mechanics: Format, Fee, Timing, and Scoring
| Item | DEA-C01 Detail |
|---|---|
| Issuer | AWS |
| Delivery | Pearson VUE, test center or 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 |
| Validity | Three years |
| Languages listed | English, Japanese, Korean, Simplified Chinese |
How the question styles work
The exam 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 responses among five or more options. Because multiple-response items require you to identify every correct option, read the stem for phrases that signal how many answers to select and eliminate options that contradict a stated constraint such as cost, latency, or operational overhead.
What the 720 really means
The passing score is a scaled score of 720 on a 100-1,000 scale. It is not a 72% raw-score threshold, and you cannot convert a practice-test percentage into the official scaled score. Scoring is compensatory across the whole exam: you do not need to pass each domain separately, so a weaker area can be offset by a stronger one. Unanswered items count as incorrect and there is no penalty for guessing, so answer everything. Fifteen of the 65 questions are unscored, and you cannot tell which, so treat each one seriously. For a deeper breakdown, read DEA-C01 Passing Score 2026.
The Four Content Domains
The exam guide organizes scored content into four domains containing 17 task statements and 120 numbered skills. The weights below are the published percentages of scored content. The guide also notes that its lists are not exhaustive. Our DEA-C01 Exam Domains guide walks through each area in more depth.
Domain 1: Data Ingestion and Transformation (34%)
The largest domain covers four tasks: performing data ingestion, transforming and processing data, orchestrating data pipelines, and applying programming concepts.
- Streaming and batch reads: sources such as Kinesis, Amazon MSK, DynamoDB Streams, DMS, Glue, S3, EMR, Redshift, Lambda, and AppFlow.
- Triggering and scheduling: EventBridge, Apache Airflow (MWAA), S3 Event Notifications, time-based jobs and crawlers.
- Operational concerns: throttling and rate limits for DynamoDB, RDS, and Kinesis; stream fan-in and fan-out; replayability of ingestion pipelines; stateful versus stateless transactions.
- Transformation: converting formats such as .csv to Apache Parquet, connecting through JDBC and ODBC, optimizing processing cost, debugging failures, and integrating LLMs into processing.
- Orchestration: Lambda, EventBridge, MWAA, Step Functions, and Glue workflows, plus alerting with SNS and SQS.
- Programming and deployment: Lambda concurrency tuning, infrastructure as code with CloudFormation and CDK, SAM packaging, CI/CD concepts, and engineering practices like version control, testing, logging, and monitoring.
Domain 2: Data Store Management (26%)
Four tasks: choosing a data store, understanding data cataloging systems, managing the lifecycle of data, and designing data models and schema evolution.
- Store selection: matching Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis Data Streams, and MSK to cost and access-pattern needs.
- Modern use cases: HNSW indexing in Aurora PostgreSQL, fast key/value access in MemoryDB, vector index types (HNSW, IVF), and Apache Iceberg open table formats.
- Cataloging: the Glue Data Catalog, Hive metastore, Glue crawlers, partition synchronization, and business catalogs via SageMaker Catalog.
- Lifecycle: S3 Lifecycle tiering and expiry, S3 versioning, DynamoDB TTL, loading and unloading between S3 and Redshift, and deletion for legal requirements.
- Modeling: schema design for Redshift, DynamoDB, and Lake Formation; partitioning and compression; lineage tracking; and vectorization concepts tied to Amazon Bedrock knowledge bases.
Domain 3: Data Operations and Support (22%)
Four tasks: automating data processing, analyzing data, maintaining and monitoring pipelines, and ensuring data quality.
- Automation: MWAA, Step Functions, SDK access, Lambda, EventBridge scheduling, and querying with Athena.
- Analysis: Glue DataBrew, QuickSight, Athena SQL and Spark notebooks, SageMaker Data Wrangler, and provisioned versus serverless tradeoffs.
- Monitoring: CloudTrail, CloudWatch Logs, log analysis with Athena, OpenSearch Service, and CloudWatch Logs Insights.
- Quality: checking for empty fields, DataBrew quality rules, consistency investigation, data sampling, 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.
- Authentication: VPC security groups, IAM roles and policies, Secrets Manager credential rotation, S3 Access Points, and PrivateLink.
- Authorization: custom least-privilege IAM policies, Lake Formation permissions across Redshift, EMR, Athena, and S3, and role-, tag-, and attribute-based approaches.
- Encryption and masking: KMS keys, cross-account encryption, and anonymization to satisfy policy.
- Audit and privacy: CloudTrail, CloudTrail Lake, PII identification with Macie, Region restrictions on replication, AWS Config inspection, and data sovereignty.
Because Domain 1 is the biggest slice, ingestion, transformation, and orchestration deserve proportionally more of your attention. This does not mean the other domains can be skipped: scoring is compensatory, but 66% of scored content sits outside Domain 1.
Services You Will Meet in the Scope
AWS publishes an in-scope service list and an out-of-scope list, and both are described as non-exhaustive and subject to change. In-scope services span Analytics (Athena, EMR, Glue, Glue DataBrew, Lake Formation, Kinesis Data Firehose, Kinesis Data Streams, Managed Service for Apache Flink, MSK, OpenSearch Service, Amazon Quick, SageMaker AI), Application Integration (AppFlow, EventBridge, MWAA, SNS, SQS, Step Functions), Database (DynamoDB, RDS, Aurora, Redshift, DocumentDB, Keyspaces, MemoryDB, Neptune), Storage (S3, S3 Glacier, S3 Tables, EBS, EFS, AWS Backup), and Security (IAM, KMS, Macie, Secrets Manager, Shield, WAF), among others.
Out-of-scope examples include Amazon FinSpace, the AWS IoT family, AWS Elemental media services, AWS Amplify, AWS AppSync, and AWS X-Ray. Spending time on those is a poor use of your preparation hours. The exam also excludes machine-learning model training and inference, programming-language-specific syntax, and drawing business conclusions from data. Languages like Python, SQL, Scala, R, Java, Bash, and PowerShell appear in the skills list as engineering tools, not as syntax tests.
Who Should Sit the Exam
AWS describes the target candidate as someone with two to three years of experience in data engineering or data architecture and one to two years of hands-on AWS experience. That is guidance, not an admission rule. There is no required prior certification, degree, mandatory course, or work-hour total. The general minimum candidate age is 13, with parent or guardian consent required for ages 13-17. See DEA-C01 Requirements 2026 for the eligibility details.
The knowledge profile the guide expects includes setting up and maintaining ETL pipelines, applying programming concepts independent of language, using Git and source control, understanding data lakes and core networking, storage, and compute, and grasping vector concepts. On the AWS side: choosing services for cost, performance, and functionality, writing SQL, transforming data, ensuring quality and consistency, and securing data.
Typical job families that value the credential include data engineer, analytics engineer, ETL developer, data platform engineer, and cloud data architect roles. The credential signals AWS-specific pipeline and data-store knowledge, but it does not guarantee a salary premium; no verified credential-specific figure was found. If career outcomes are your main question, read DEA-C01 Jobs, the DEA-C01 Salary Guide, and Is the DEA-C01 Certification Worth It? to weigh the evidence.
Registration, Languages, and Accommodations
You book through AWS Certification with Pearson VUE as the delivery partner. The fee is USD 150 plus applicable taxes. No member versus non-member price schedule is published for this credential. If you hold an active AWS Certification, you receive a 50% discount benefit for a next certification exam, but this is a specific benefit, not an unrestricted discount or a guaranteed price for every candidate. The full cost picture is in DEA-C01 Certification Cost 2026, and scheduling guidance is in DEA-C01 Exam Dates 2026.
- Languages: English, Japanese, Korean, and Simplified Chinese are listed.
- ESL accommodation: an eligible +30-minute accommodation must be requested before booking. It is not silently added to the standard timer.
- Breaks: there are no scheduled breaks. At a test center, unscheduled breaks consume exam time. Online candidates cannot leave camera view for an unapproved break.
Validity and Renewal
The credential is valid for three years. The AWS credential page describes renewal by passing the latest version of the exam. The dedicated AWS Recertification table additionally lists AWS Certified Generative AI Developer - Professional as an option that renews an active Data Engineer Associate certification for three years. The renewed period runs from completion of the recertification 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 assume options shown for other certifications apply here.
DEA-C01 Compared With SAA-C03
Many candidates weigh this exam against the Solutions Architect Associate (SAA-C03). They are different credentials with different objectives; SAA-C03 is comparison context only here.
| Dimension | DEA-C01 | SAA-C03 (context) |
|---|---|---|
| Role focus | Data pipelines, data stores, data operations, data governance | Broad architecture design across workloads |
| Heaviest topic area | Ingestion and transformation (34%) | Different domain structure; not covered in this article |
| Typical services in focus | Glue, Kinesis, MSK, Redshift, Athena, Lake Formation, EMR, MWAA | Wider compute, networking, and resilience services |
| Best fit | Engineers building and running data platforms | Engineers designing general AWS architectures |
If your daily work is pipelines and analytics, DEA-C01 aligns more directly. If your work is broader infrastructure design, the architect track may fit better. Many practitioners eventually pursue both.
Sequencing Your Preparation by Domain
You do not need a generic study system here, just a sensible order that reflects the exam weights and how the topics build on each other. The DEA-C01 Study Guide goes further, and the DEA-C01 Cheat Sheet helps with last-pass review.
Domain 1: Ingestion, Transformation, Orchestration
- Start here because it is 34% of scored content and its concepts feed everything else.
- Compare Kinesis Data Streams, Firehose, and MSK; practice replayability, throttling, and fan-out reasoning.
- Work through Glue, EMR, and Lambda transformations, and Step Functions versus MWAA versus Glue workflows.
Domain 2: Data Store Management
- Learn access-pattern-driven store selection across Redshift, DynamoDB, RDS, and Aurora.
- Cover the Glue Data Catalog, crawlers, partitions, S3 Lifecycle, Iceberg, and vector index concepts.
Domain 3: Data Operations and Support
- Focus on Athena, DataBrew quality rules, CloudWatch and CloudTrail troubleshooting, and handling data skew.
Domain 4: Security and Governance
- Review IAM least privilege, Lake Formation permissions, KMS, Macie, and Region-restriction controls.
- Finish with mixed-domain practice and timed runs against the 130-minute limit.
This sequence is a suggestion, not an AWS requirement. Adjust it to your experience: someone already fluent in Redshift and IAM can compress those weeks and spend longer on streaming and orchestration. Use the DEA-C01 practice tests to find gaps, and revisit How Hard Is the DEA-C01 Exam? to calibrate your expectations.
Open Questions in the Published Scope
Careful candidates should know where the published materials are not perfectly tidy:
- 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 AWS DMS Schema Conversion. Both observations exist in the sources. Learn DMS Schema Conversion well and treat SCT as lower-confidence until AWS clarifies.
- Quick versus QuickSight: the service inventory says Amazon Quick, while Skills 3.2.1-3.2.2 say QuickSight. Both labels are published; do not assume anything beyond what AWS states.
- S3 Tables: the storage inventory lists Amazon S3 Tables twice. That is a duplicate listing, not an extra objective.
Check the official exam guide before booking, since lists can change. Practice material from any vendor, including this site, is independently written knowledge preparation, not actual exam questions, and it does not guarantee a pass or measure on-the-job competence. For a plain-language orientation to the credential, you can also see What Is DEA-C01 Certification?.
FAQ
It is the exam code for the AWS Certified Data Engineer - Associate certification. The exam validates skills in data ingestion and transformation, data store management, data operations and support, and data security and governance on AWS.
There are 65 questions, of which 50 are scored and 15 are unscored and unidentified. You have 130 minutes, with no scheduled breaks.
A minimum scaled score of 720 on a 100-1,000 scale. This is not a 72% raw score, scoring is compensatory across the exam, and there is no penalty for guessing.
No. AWS requires no prior certification, degree, or mandatory course. The recommendation of two to three years in data engineering and one to two years on AWS is guidance rather than an admission rule.
The fee is USD 150 plus applicable taxes. The credential is valid for three years and can be renewed by passing the latest version of the exam or, per the AWS recertification table, by passing AWS Certified Generative AI Developer - Professional.