AWS Certified Data Engineer Associate: Exam Guide
A guide to the AWS Certified Data Engineer - Associate exam, DEA-C01, taken from the official exam guide: format, length, cost and passing score, the four content domains and their weightings, what is out of scope, the services to know, and a study plan organised by domain with hands-on practice for each.
GloryTecks Team
Training & Placement Faculty
Last updated: 1 October 2026
The AWS Certified Data Engineer - Associate, exam code DEA-C01, is the AWS certification aimed squarely at data engineering work. This guide sets out what the exam is and how to prepare, taken from AWS's own exam guide and certification page. Exam details change; check both before you book.
The exam in numbers
| Item | Detail |
|---|---|
| Length | 130 minutes |
| Questions | 65, multiple choice or multiple response |
| Scored questions | 50; the other 15 are unscored and not identified |
| Score | Scaled, 100 to 1,000 |
| Passing score | 720 |
| Cost | 150 USD |
| Validity | 3 years |
The length, question count, cost and validity are from the certification page (AWS: Data Engineer - Associate); the scoring details are from the exam guide (AWS exam guide: DEA-C01).
Three details in the guide affect how you sit the exam:
- No penalty for guessing. Unanswered questions are scored as incorrect, so answer everything.
- Compensatory scoring. You do not need to pass each section, only the exam as a whole. A weak domain can be offset by a strong one.
- Unscored questions look like the rest. Do not try to spot them.
Who it is for
The guide describes a target candidate with the equivalent of 2 to 3 years of experience in data engineering and at least 1 to 2 years of hands-on experience with AWS services. That is a description of the intended audience, not an entry requirement. It does tell you how the questions are pitched: at someone who has built pipelines and can compare services by cost, performance and function.
Expected general knowledge includes setting up and maintaining ETL pipelines, language-agnostic programming concepts, Git, data lakes, and general networking, storage and compute.
Out of scope, according to the guide: performing ML training and inference, knowledge of language-specific syntax, and drawing business conclusions from data. You will not be asked to write code.
The four domains
| Domain | Weight | Tasks |
|---|---|---|
| 1. Data Ingestion and Transformation | 34% | Perform data ingestion; transform and process data; orchestrate data pipelines; apply programming concepts |
| 2. Data Store Management | 26% | Choose a data store; understand data cataloging systems; manage the lifecycle of data; design data models and schema evolution |
| 3. Data Operations and Support | 22% | Automate data processing; analyse data with AWS services; maintain and monitor pipelines; ensure data quality |
| 4. Data Security and Governance | 18% | Authentication; authorisation; encryption and masking; logs for audit; data privacy and governance |
Sixty percent of the scored content is in the first two domains. They are also the two closest to daily data engineering, which is why hands-on practice is the most efficient preparation.
Services to know
The in-scope list is long. Group it by job, which is how questions are framed (AWS exam guide: in-scope services):
- Ingest: Kinesis Data Streams, Firehose, MSK, AppFlow, DMS.
- Store: S3, Redshift, DynamoDB, RDS and Aurora.
- Catalog and govern: Glue Data Catalog, Lake Formation.
- Transform: Glue, EMR, Lambda, Managed Service for Apache Flink.
- Query and analyse: Athena, Redshift, OpenSearch Service.
- Orchestrate: Step Functions, MWAA, EventBridge, Glue workflows.
- Secure and operate: IAM, KMS, CloudWatch, CloudTrail.
The exam's typical question gives a scenario and asks which service or configuration meets it at lowest cost or with least operational effort. Knowing what each service is matters less than knowing where one stops and the next begins: Kinesis or MSK, Glue or EMR, Athena or Redshift.
A study plan by domain
Work through the domains in order of weight, and build something for each.
Domain 1 (34%). Ingest a file and a stream; transform both; orchestrate the result. Reading: Amazon Kinesis explained, the AWS Glue tutorial and AWS Step Functions.
Domain 2 (26%). Choose stores for given access patterns; catalogue data; set lifecycle rules; design a schema. Reading: Amazon Redshift explained and the Glue Data Catalog and crawlers.
Domain 3 (22%). Query with Athena, monitor a pipeline, add data quality checks. Practise reading what a failed job logs.
Domain 4 (18%). IAM roles and policies, encryption with KMS, Lake Formation permissions, audit logging. This domain is the one people skip, and at 18 percent it decides borderline results.
How to prepare
- Read the exam guide first, including the task statements under each domain. It is the specification.
- Build one end-to-end pipeline in your own account: land raw files in S3, catalogue them, transform to partitioned Parquet with Glue, query with Athena, orchestrate with Step Functions. Set a budget alert before you start.
- Learn the comparisons. For each pair of similar services, write down when you would choose each.
- Use AWS's own practice questions, which the certification page links to, to learn the question style.
- Review wrong answers by domain, and return to the documentation for that task.
- Book the exam once practice results are steady, not before.
How long this takes depends on your experience. Someone already running pipelines on AWS is mostly learning the services they have not used. Someone new to AWS is learning a platform, and should plan for months, with the AWS roadmap for data engineers as the longer route.
Is it worth it?
A certification shows that you have studied a defined body of knowledge and passed a proctored exam. It does not show that you can build and operate a pipeline; a project does that. The two together are stronger than either. If you are choosing where to spend limited time, build first, then certify what you have learned. The AWS certification path for data roles covers which certifications to consider and in what order.
How many questions are on the AWS Data Engineer Associate exam?
What is the passing score for DEA-C01?
Do I need coding skills for the exam?
Is there a prerequisite certification?
The AWS data services this exam covers, including S3, Glue, Redshift, EMR and Kinesis, are taught in the Data Engineering course.
Sources
- AWS Certified Data Engineer - Associate (DEA-C01) exam guide (AWS Certification Exam Guides) — AWS Documentation
- AWS Certified Data Engineer - Associate (Amazon Web Services) — Amazon Web Services
- Content Domain 1: Data Ingestion and Transformation (AWS Certification Exam Guides) — AWS Documentation
- Content Domain 2: Data Store Management (AWS Certification Exam Guides) — AWS Documentation
- In-Scope AWS Services (AWS Certification Exam Guides) — AWS Documentation
GloryTecks Team
Training & Placement Faculty
The GloryTecks faculty team mentors learners across Data Science, AI and Data Engineering, with a focus on real-time projects and placement readiness in Hyderabad.
Want to master AWS?
Join GloryTecks’ expert-led Data Engineering training in Hyderabad with real-time projects and 100% placement support.
Related articles
AWS Roadmap 2026 for Data Engineers
What to learn on AWS as a data engineer, in the order data moves through a pipeline: S3 and IAM, Glue and Athena, Glue jobs, Redshift, Step Functions, streaming and governance, with something to build at every stage and a clear test for when you are done.
Amazon Redshift Explained: The Cloud Data Warehouse
An introduction to Amazon Redshift, AWS's data warehouse: how a leader node, compute nodes and slices run a query in parallel, why data is stored in columns, how managed storage separates compute from storage, Serverless versus a provisioned cluster, how it differs from PostgreSQL, and what to learn first.
AWS Glue Data Catalog and Crawlers Explained
The Glue Data Catalog is the index of an AWS data lake, and crawlers are the usual way to fill it. This guide explains what the catalog stores, how a crawler classifies files and decides between tables and partitions, what three real files with drifting schemas show, and the settings that keep crawlers predictable.