AWS Certified Data Engineer - Associate (DEA-C01) Practice Test

The target candidate is a data engineer or data architect with roughly 2–3 years of experience in data engineering and at least 1–2 years of hands-on AWS experience. This includes professionals who design and maintain ETL/ELT pipelines, manage data lakes and warehouses, or work with real-time streaming architectures. Adjacent roles transitioning into cloud data engineering — such as database administrators, backend developers, or traditional ETL developers — will also find this certification a clear roadmap for bridging legacy skills with AWS-native approaches.

The exam suits those who regularly work with concepts such as volume, variety, and velocity of data; data modeling and schema design; data lifecycle management; and cloud security and governance. It is not aimed at data scientists, ML engineers, or business analysts, as those domains fall outside the exam's scope.

AWS does not enforce formal prerequisites for the DEA-C01, but the official exam guide recommends 2–3 years of data engineering or data architecture experience and 1–2 years of hands-on work with AWS services. Candidates should be comfortable setting up and maintaining ETL pipelines from ingestion to destination, writing and executing SQL queries, using Git-based source control workflows, and applying language-agnostic programming concepts (loops, conditionals, data structures).

On the AWS side, recommended knowledge includes familiarity with data pipeline orchestration services (AWS Glue, AWS Step Functions), storage systems (Amazon S3, Amazon Redshift, Amazon DynamoDB), streaming platforms (Amazon Kinesis), and security/governance services (AWS IAM, AWS KMS, AWS Lake Formation). Understanding of data lakes, networking fundamentals (VPC, subnets, connectivity), compute options (Amazon EMR, AWS Lambda), and vector/embedding concepts is also beneficial. While no prior AWS certification is required, having the AWS Cloud Practitioner or AWS Solutions Architect – Associate background provides a useful foundation.

The DEA-C01 exam consists of 65 total questions: 50 scored questions that contribute to the final result and 15 unscored pilot questions that AWS uses to evaluate future content. Unscored questions are not identified, so candidates should treat all questions equally. Question types are multiple choice (one correct answer from four options) and multiple response (two or more correct answers from five or more options). The time limit is 130 minutes, and the exam is delivered via Pearson VUE at a testing center or through an online proctored session. The exam is available in English, Japanese, Korean, and Simplified Chinese, and costs $150 USD.

Scores are reported on a scaled range of 100–1,000, and the minimum passing score is 720. AWS uses a compensatory scoring model, meaning candidates do not need to achieve a passing threshold in each individual domain — only the overall scaled score matters. Unanswered questions are treated as incorrect; there is no penalty for guessing. The certification is valid for three years, after which recertification requires passing the current version of the exam.

• 1.Domain 1 – Data Ingestion and Transformation (34%): Covers reading from and writing to diverse data sources (batch and streaming), using AWS Glue for ETL jobs and crawlers, ingesting data with Amazon Kinesis Data Streams and Kinesis Firehose, applying transformation logic with AWS Lambda and Amazon EMR, and orchestrating pipelines with AWS Step Functions and Amazon MWAA. Also includes applying programming concepts (language-agnostic) to automate pipeline tasks.
• 2.Domain 2 – Data Store Management (26%): Covers selecting appropriate storage services (Amazon S3, Amazon Redshift, Amazon DynamoDB, Amazon RDS, Amazon OpenSearch Service) based on access patterns, latency, cost, and scale. Includes designing data models, implementing partitioning and compression strategies, managing schema evolution with AWS Glue Data Catalog, and enforcing data lifecycle policies (S3 lifecycle rules, Redshift data sharing).
• 3.Domain 3 – Data Operations and Support (22%): Covers operationalizing and monitoring data pipelines using Amazon CloudWatch, AWS CloudTrail, and AWS Glue job metrics. Includes automating pipeline deployments with AWS CDK or CloudFormation, troubleshooting pipeline failures, performing data quality checks and validation, and analyzing data using Amazon Athena and Amazon Redshift queries.
• 4.Domain 4 – Data Security and Governance (18%): Covers implementing authentication and authorization using AWS IAM, resource-based policies, and AWS Lake Formation column- and row-level security. Includes encrypting data at rest and in transit with AWS KMS and SSL/TLS, managing data privacy and compliance requirements, configuring audit logging via AWS CloudTrail, and applying data governance frameworks using AWS Glue Data Catalog and Lake Formation.

• Start with the official AWS Exam Guide (PDF from awsstatic.com) and map every task statement to your existing knowledge gaps before studying anything else — prioritize Domain 1 (34%) and Domain 2 (26%) since they together account for 60% of the scored content.
• Use AWS Skill Builder's official 'AWS Certified Data Engineer – Associate' exam prep course, which includes video modules aligned to each domain and the Official Practice Question Set with rationales explaining both correct and incorrect answers.
• Build hands-on labs around the core data pipeline services: create an end-to-end pipeline using AWS Glue crawlers and ETL jobs, write to Amazon Redshift, and stream real-time data through Amazon Kinesis Data Firehose into S3 — this covers the three most heavily tested service categories.
• Take the AWS Certification Official Pretest on Skill Builder early in your study cycle to identify weak domains, then revisit it after studying to measure progress before scheduling the real exam.
• For Domain 4 (Security and Governance), specifically practice configuring AWS Lake Formation permissions, column-level security, and the difference between IAM-based vs. Lake Formation–based access control, as these are frequently tested and commonly misunderstood.
• Review the AWS Well-Architected Framework's Data Analytics Lens and the AWS whitepapers on data lake architecture and streaming data solutions — these provide the architectural reasoning behind best-practice questions on the exam.
• Practice with multiple-response question formats specifically, since these are harder to guess and require knowing all correct answers. Use timed practice sets to simulate the 130-minute constraint across 65 questions (roughly 2 minutes per question).

The DEA-C01 certification targets one of the fastest-growing roles in cloud computing. AWS-certified data engineers in the US report average salaries around $141,000 per year according to Glassdoor data, with entry-level positions starting near $124,000–$130,000 and senior roles exceeding $175,000. Research from the Jefferson Frank Careers and Hiring Guide found that 73% of AWS professionals saw a salary increase after certification, averaging approximately 27%. Job roles accessible with this credential include Data Engineer, Cloud Data Architect, ETL/ELT Developer, Data Platform Engineer, and Analytics Engineer.

AWS certifications appear in cloud job postings more than any other vendor credential, and the DEA-C01 specifically validates the services — Glue, Redshift, Kinesis, S3 — that dominate real-world data engineering job requirements. For professionals transitioning from database administration, backend development, or traditional ETL roles, the certification provides a structured path into cloud-native data engineering. Many candidates report role transitions or salary increases within 3–6 months of earning the credential. Pairing DEA-C01 with the Databricks Data Engineer Associate certification is widely considered the most job-market-relevant two-certification combination in the data engineering space.