AWS Certified Data Engineer Associate (DEA‑C01) Practice Exams
About the AWS DEA-C01 exam
Exam at a glance
AWS's associate-tier data engineering exam, released March 2024 as the successor to the retired Database Specialty.
Who this exam is for
DEA-C01 targets data engineers building production pipelines on AWS — ingestion, transformation, storage, orchestration, monitoring. Strong fit for data engineers, analytics engineers, and ETL developers with 2-3 years of data-engineering experience and 1-2 years on AWS.
Domain weighting
- Data Ingestion and Transformation: ~34%
- Data Store Management: ~26%
- Data Operations and Support: ~22%
- Data Security and Governance: ~18%
Core services (most-tested)
- AWS Glue — ETL jobs (Spark + Python shell), crawlers, Data Catalog, DataBrew, bookmarks, workflows.
- Amazon S3 — storage classes, lifecycle, partitioning strategies, S3 Select, Lake Formation permissions.
- Amazon Redshift — Spectrum, materialized views, workload management, RA3 nodes, COPY/UNLOAD.
- Amazon Athena — federated queries, partition projection, CTAS, workgroups, cost controls.
- Streaming — Kinesis Data Streams, Kinesis Data Firehose, Amazon MSK and MSK Connect.
- Orchestration — Step Functions, MWAA (Airflow), EventBridge Scheduler, Glue Workflows.
- AWS DMS — full-load + CDC, source/target engine matrices, schema conversion.
- Amazon EMR — Spark, Hive, Presto, EMR Serverless, instance fleets, Spot strategies.
- Lake Formation + Glue Data Catalog — fine-grained permissions, LF-tags, row/column filtering.
Prerequisites
AWS recommends 2-3 years of data-engineering experience and 1-2 years of hands-on AWS experience. No formal prerequisites — Cloud Practitioner is not required. SQL fluency, Python (PySpark) or Scala, and orchestrator familiarity (Airflow or Step Functions) are the strongest accelerators.
Why take this certification
- Replaces the retired Database Specialty. DEA-C01 is the modern AWS credential for data practitioners — broader than DBS, covering streaming, ETL, lakehouse, and governance instead of pure database administration.
- High demand. Data engineering remains one of the fastest-growing roles in cloud. Average US salary for AWS-certified data engineers sits in the $130,000–$160,000 range depending on experience and seniority.
- Complements the ML track. Pairs naturally with the sibling MLA-C01 Machine Learning Engineer Associate — data engineers build the pipelines that feed ML training and inference.
- Practical, pipeline-first skills. The exam validates day-to-day work: choosing between Glue and EMR, picking the right Kinesis service, partitioning S3 for Athena cost, designing Redshift workloads — all decisions you'll make on real projects.
What you'll learn in the DEA-C01 exam
DEA-C01 validates that you can build, operate, and optimize production data pipelines on AWS. The exam is scenario-driven — most questions describe a workload with constraints (latency, cost, schema evolution, governance) and ask you to choose the architecture that fits.
Ingestion
- Streaming: Kinesis Data Streams (shards, enhanced fan-out, KCL), Kinesis Data Firehose (buffering, dynamic partitioning, format conversion), Amazon MSK and MSK Connect.
- Batch + CDC: AWS DMS for full-load and change data capture, AWS Glue ETL jobs, AWS Lambda for lightweight event-driven loads.
- Orchestration of ingestion: Step Functions, EventBridge rules, S3 event notifications, Glue Workflows.
Transformation
- Glue DataBrew for low-code visual transforms; Glue ETL (Spark) for code-first PySpark and Scala jobs.
- EMR Spark for heavy distributed processing; EMR Serverless for elastic Spark without cluster management.
- AWS Lambda for sub-minute transforms and event-driven enrichment.
- Amazon Athena for ad-hoc SQL transforms and CTAS output to S3.
Storage
- S3: storage classes, lifecycle rules, partitioning, Intelligent-Tiering, Lake Formation governance.
- Lake Formation: table and column-level permissions, LF-tags, blueprint-based ingestion.
- Redshift: RA3 vs DC2, distribution styles, sort keys, materialized views, Spectrum to query S3 in place.
- RDS, DynamoDB, Timestream, DocumentDB — when each is the right operational store.
Orchestration
- MWAA (Managed Workflows for Apache Airflow) for DAG-based pipelines with rich Python.
- Step Functions for state-machine orchestration with native AWS service integrations.
- EventBridge Scheduler for cron-style and one-time triggers at scale.
Analytics serving
- Athena for serverless SQL over S3; Redshift Spectrum for joining warehouse data with the lake; QuickSight for BI and dashboards (SPICE, ML insights).
Data governance
- Lake Formation permissions (LF-tags, row/column filtering); Glue Data Catalog as the single metadata store.
- IAM least-privilege design; KMS envelope encryption; Macie for PII discovery; CloudTrail data events for S3 and Lambda audit logging.
How the practice exams help
Each free question and every premium exam mirrors the scenario-style format AWS uses — long stem, four to six plausible options, one or two correct. Detailed explanations cover not just why the right answer is right but why the distractors are wrong, so you learn the trade-offs between Glue vs EMR, Firehose vs Streams, Athena vs Redshift, Step Functions vs MWAA — rather than memorizing answers.
How to prepare for the DEA-C01 exam
A successful DEA-C01 preparation strategy combines theoretical study, hands-on pipeline building, and exam simulation. Recommended approach:
- Study AWS data services (4–5 weeks). Review the official AWS DEA-C01 exam guide and the AWS Skill Builder DEA-C01 learning path. Focus on Glue, S3, Redshift, Athena, Kinesis, and Lake Formation first — these dominate the ingestion, transformation, and storage domains.
- Hands-on labs (3–4 weeks). Create a free-tier AWS account and build real pipelines. Land Kinesis Firehose data into S3 partitioned by date, crawl with Glue, query with Athena, and load into Redshift via COPY. Build a Step Functions or MWAA DAG that orchestrates a multi-stage ETL. Implement Lake Formation row/column permissions on a sample dataset. Hands-on experience is critical for passing scenario-based questions.
- Review AWS analytics whitepapers (1 week). Read the AWS data analytics lens of the Well-Architected Framework, the Lake House architecture whitepaper, and the Glue + Redshift best-practice guides. These documents align directly with exam content.
- Practice exams (1–2 weeks). Take timed practice tests to identify weak areas. Detailed explanations on every answer option help you learn the reasoning, not just memorize answers. Aim for consistent 80%+ scores before scheduling your exam.
Recommended timeline
8–12 weeks of focused study (10–15 hours per week) for working data engineers with some AWS experience. Beginners without data-engineering background should allow 12–16 weeks and prioritize SQL, Python, and one orchestrator (Airflow or Step Functions) before tackling the AWS-specific material.
Official resources
Download the official AWS DEA-C01 exam guide and follow the Skill Builder DEA-C01 learning path. For deeper service understanding, AWS's free training portal hosts the Data Engineer learning plan and dozens of self-paced labs.
Frequently asked questions
Are these actual exam questions?
No. All Nex Arc AWS DEA-C01 practice questions are original content based on the provider's published exam blueprint, official documentation, and real-world scenarios across every topic the exam covers. We do not offer brain dumps, leaked questions, or actual exam content. Using or distributing real exam questions violates certification provider agreements and can result in certification revocation.
How many questions are in the AWS DEA-C01 exam?
The AWS DEA-C01 exam consists of 65 questions that you need to complete in 130 minutes. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers).
What's the DEA-C01 passing score?
The passing score is 720 out of 1000. AWS uses a scaled scoring model, and not all questions carry the same weight. Focus on understanding the trade-offs between data pipeline services rather than memorizing answers.
How do I access the full DEA-C01 practice exam?
Click on the Buy Now button in the sidebar to purchase the complete AWS DEA-C01 course. After payment, you'll have instant access to 12 practice exams with detailed explanations and lifetime access.
What are the prerequisites for the DEA-C01 exam?
AWS recommends 2-3 years of data engineering experience and 1-2 years of hands-on AWS experience, but there are no formal prerequisites. Strong SQL skills, proficiency in Python or Scala, and familiarity with at least one orchestrator (Airflow, Step Functions) are highly recommended. Background in batch and streaming data processing, ETL/ELT design, and data modeling will accelerate preparation. Beginners without data engineering experience should expect 12-16 weeks of focused study.
How long is the DEA-C01 certification valid?
AWS certifications are valid for three years from the date you pass the exam. To recertify DEA-C01, you can either retake the current version of the exam or pass a higher-tier exam in the same track. A 50% discount voucher for the recertification exam is available through your AWS Certification Account. AWS does NOT offer a Skill Builder credits or continuing-education recertification path for Associate-level certifications — exam-based recertification is the only option.
What's the exam cost and can I retake it if I fail?
The DEA-C01 exam costs $150 USD globally (converted to local currency). The exam is delivered through Pearson VUE at testing centers or via online proctoring. If you fail, you must wait 14 days before retaking the exam at full price. There is no limit to the number of attempts, but each attempt requires a new $150 payment. AWS occasionally offers 50% discount vouchers for recertification or through AWS Partner Network programs. Practice thoroughly before attempting to minimize retake costs.
What AWS services are most heavily tested on DEA-C01?
Based on the four exam domains, the most frequently tested services are: AWS Glue (ETL jobs, crawlers, Data Catalog, DataBrew), Amazon S3 (storage classes, lifecycle, partitioning, Lake Formation), Amazon Redshift (Spectrum, materialized views, workload management, RA3 nodes), Amazon Athena (federated queries, partition projection, CTAS), Amazon Kinesis (Data Streams, Firehose) and MSK for streaming ingestion, AWS Lambda (event-driven transforms), AWS Step Functions and MWAA (orchestration), AWS DMS (database migration and CDC), and Amazon EMR (Spark, Hive). Lake Formation and Glue Data Catalog appear across nearly every governance scenario.
How does DEA-C01 differ from MLA-C01?
DEA-C01 (Data Engineer Associate) and MLA-C01 (Machine Learning Engineer Associate) are complementary, not overlapping. DEA-C01 covers the data lifecycle: ingestion, transformation, storage, orchestration, governance, and analytics serving — essentially everything that lands data in a state where it can be consumed. MLA-C01 starts where DEA-C01 ends: feature engineering, model training, deployment, monitoring, and MLOps on services like SageMaker, Bedrock, and Comprehend. A data engineer typically owns the pipelines that produce features; an ML engineer owns the models that consume them. Many teams hire both roles, and either certification reinforces the other.
Can I retake the DEA-C01 exam if I fail?
Yes. AWS allows retakes after a 14-day waiting period. You pay the full $150 exam fee again. There is no limit on total attempts.