DEA-C01 — AWS Certified Data Engineer - Associate
Amazon

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

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100 questions
June 14, 2026 updated
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Domain coverage

  • Data Ingestion and Transformation (34%)
  • Data Store Management (26%)
  • Data Operations and Support (22%)
  • Data Security and Governance (18%)

Sample Questions (10 of 100 shown)

Q1 Data Ingestion and Transformation (34%)
A financial analytics company stores transaction data in large CSV files in Amazon S3. They need a solution for data engineers to run SQL queries directly on the CSV files without loading into a database. Which AWS service should they use?
  1. Amazon Redshift Spectrum
  2. AWS Data Pipeline
  3. Amazon Athena
  4. Amazon EMR with Hive
✓ Correct Answer: C
Amazon Athena is an interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It is serverless and directly queries data in S3 without loading it into a separate database.
Q2 Data Ingestion and Transformation (34%)
A data engineer needs to reformat CSV data in S3 for more efficient analytics. Data includes timestamps, and old data is deleted daily. Which solution is the most cost-effective for converting and optimizing data?
  1. Use AWS Glue to convert CSV to Parquet partitioned by timestamp
  2. Run an Athena CTAS query to convert data to Parquet with Snappy compression partitioned by timestamp
  3. Configure an EMR Spark job to convert CSV to Parquet
  4. Set up a daily Lambda function to convert CSV to Parquet
✓ Correct Answer: B
Athena's CTAS (CREATE TABLE AS SELECT) can convert data to columnar Parquet format without managing infrastructure. Combined with Snappy compression and partitioning, it is the most cost-effective serverless solution.
Q3 Data Ingestion and Transformation (34%)
A company needs to collect IoT sensor data at 10,000 records per second for near real-time analysis and long-term storage in S3. Which AWS service combination is most appropriate?
  1. Amazon Kinesis Data Streams with AWS Lambda
  2. Amazon Kinesis Data Firehose with Amazon S3
  3. Amazon MSK with Amazon Redshift
  4. AWS Data Pipeline with Amazon EC2
✓ Correct Answer: B
Kinesis Data Firehose automatically captures and loads streaming data into S3 without custom code. It handles scaling automatically and supports near real-time delivery.
Q4 Data Ingestion and Transformation (34%)
Which Kinesis feature allows replaying data from a specific point in time within the retention period?
  1. Kinesis Data Firehose buffering
  2. Kinesis Data Streams enhanced fan-out
  3. Kinesis Data Streams record processor checkpointing
  4. Kinesis Video Streams fragmentation
✓ Correct Answer: C
Kinesis Data Streams supports replay through the Kinesis Client Library (KCL) checkpointing. Consumers can checkpoint their progress and restart from any checkpoint within the retention period.
Q5 Data Ingestion and Transformation (34%)
Multiple independent applications must read from the same clickstream data stream, each at their own pace without affecting others. Which Kinesis feature should be used?
  1. Kinesis Data Streams with shared throughput
  2. Kinesis Data Streams with enhanced fan-out
  3. Kinesis Data Firehose with multiple destinations
  4. Kinesis Analytics with multiple outputs
✓ Correct Answer: B
Enhanced fan-out provides each consumer with dedicated read throughput, allowing multiple applications to read independently without competing for throughput.
Q6 Data Ingestion and Transformation (34%)
What is the maximum retention period for data in Amazon Kinesis Data Streams?
  1. 24 hours
  2. 7 days
  3. 365 days
  4. Permanent
✓ Correct Answer: C
Kinesis Data Streams supports up to 365 days retention. The default is 24 hours, extendable to 7 days or up to 365 days for additional cost.
Q7 Data Ingestion and Transformation (34%)
A company needs to build an ETL pipeline that transforms data from S3 and loads it into Redshift. Transformations require complex business logic written in Python. Which AWS service should be used?
  1. AWS Data Pipeline with custom EC2 instances
  2. AWS Glue with PySpark jobs
  3. Amazon EMR with Hive scripts
  4. AWS Lambda with Python runtime
✓ Correct Answer: B
AWS Glue is a serverless ETL service supporting PySpark for complex transformations. It can read from S3, apply transformations, and write to Redshift without managing infrastructure.
Q8 Data Ingestion and Transformation (34%)
Which AWS Glue feature automatically discovers metadata from data sources and catalogs it?
  1. Glue ETL jobs
  2. Glue crawlers
  3. Glue DataBrew
  4. Glue Studio
✓ Correct Answer: B
AWS Glue crawlers automatically scan various data stores, extract metadata, and populate the AWS Glue Data Catalog with table definitions.
Q9 Data Ingestion and Transformation (34%)
A data engineer needs to convert JSON to Parquet format partitioned by date. Which AWS Glue feature is most appropriate?
  1. Glue crawler with custom classifiers
  2. Glue ETL job with DynamicFrames
  3. Glue DataBrew recipes
  4. Glue Studio visual job
✓ Correct Answer: B
Glue ETL jobs with DynamicFrames provide built-in support for format conversion and partitioning. DynamicFrames handle schema evolution.
Q10 Data Ingestion and Transformation (34%)
What is the purpose of AWS Glue job bookmarks?
  1. Save code versions of ETL jobs
  2. Track processed data and enable incremental processing
  3. Tag tables for deletion in the Data Catalog
  4. Create recovery points for the Data Catalog
✓ Correct Answer: B
Glue job bookmarks track previously processed data, enabling incremental processing in subsequent job runs that only handles new or changed data.

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Exam overview

The AWS Certified Data Engineer - Associate (DEA-C01) certification validates your ability to implement data pipelines, manage data stores, and monitor, troubleshoot, and optimize cost and performance on AWS. This Associate-level certification, launched in March 2024, fills a critical gap in the AWS certification portfolio by focusing specifically on the data engineering role — distinct from the broader Solutions Architect or developer-focused certifications.

The target candidate should have the equivalent of 2–3 years of experience in data engineering with at least 1–2 years of hands-on experience with AWS services. You should be comfortable setting up ETL/ELT pipelines, designing data models using relational and NoSQL databases, managing data lakes, and applying data governance best practices. The exam tests practical knowledge of AWS data services including S3, Glue, Lake Formation, Redshift, Kinesis, Athena, EMR, and DynamoDB, along with broader infrastructure services needed to support data pipelines.

Our DEA-C01 practice test product provides comprehensive preparation with 400+ exam-style questions covering all four official domains with correct weightings. Each question comes with detailed explanations that clarify both correct and incorrect answers, helping you develop the analytical thinking needed for real-world data engineering scenarios. The package includes domain-wise practice modules, full-length simulation exams (65 questions, 130 minutes) mirroring the real test environment, and a downloadable PDF study guide for offline review. Whether you're transitioning from a traditional data role or adding data engineering skills to your AWS expertise, our materials are designed to accelerate your certification success.

Official Exam Domains & Weighting

To successfully pass the DEA-C01 exam, candidates must demonstrate proficiency across the following four core domains:
  • Domain 1: Data Ingestion and Transformation (34%)
Implement data ingestion strategies using AWS services (S3, Kinesis, DMS, Appflow), build ETL/ELT pipelines with AWS Glue and Spark, orchestrate workflows using Step Functions and managed workflows, apply programming concepts to transform data, and manage schema evolution and data quality checks.
  • Domain 2: Data Store Management (26%)
Select optimal data stores for specific use cases (Amazon S3 data lake, Redshift for warehousing, DynamoDB for NoSQL, RDS for relational, OpenSearch for search), design data models and schemas, catalog data with Glue Data Catalog and Lake Formation, implement data lifecycle policies, and manage partitioning, compression, and data formats (Parquet, ORC, Avro).
  • Domain 3: Data Operations and Support (22%)
Operationalize and monitor data pipelines using CloudWatch, CloudTrail, and S3 event notifications; troubleshoot pipeline failures and performance bottlenecks; analyze data with Athena and Redshift Spectrum; implement data quality frameworks; and automate routine data operations with Lambda and EventBridge.
  • Domain 4: Data Security and Governance (18%)
Implement authentication and authorization for data services using IAM policies and Lake Formation permissions; encrypt data at rest and in transit with KMS and S3 encryption; apply data privacy controls including data masking and PII redaction; enable audit logging; and implement governance frameworks using AWS Config, Service Catalog, and Macie.

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Frequently Asked Questions

While SAA-C03 covers broad architectural design across all AWS services, DEA-C01 is purpose-built for data engineers. It goes deeper into data-specific services like AWS Glue, Lake Formation, Kinesis, and Redshift, with a focus on pipeline implementation, data modeling, and ETL/ELT workflows. SAA-C03 is a prerequisite for many, but DEA-C01 is the certification that validates you can build and operate data pipelines, not just design architectures. Many professionals earn SAA-C03 first, then pursue DEA-C01 to specialize.

The exam heavily tests data ingestion and processing services including AWS Glue (ETL jobs, crawlers, Data Catalog), Amazon Kinesis (Data Streams, Firehose, Analytics), Amazon S3 (as a data lake with partitioning and compression), Amazon Redshift (warehousing, Spectrum, Redshift Serverless), and AWS DMS (database migration). For data stores, expect DynamoDB, RDS, OpenSearch Service, and Amazon EMR. Security and governance topics focus on Lake Formation, IAM, KMS, and AWS Macie. Understanding when to use each service for specific data workloads is critical.

AWS recommends 2–3 years of data engineering experience with 1–2 years of hands-on AWS experience. You should understand ETL/ELT pipeline concepts, data modeling (star schema, data vault, 3NF), SQL querying, and basic programming concepts (Python or Scala are helpful). Familiarity with Git for source control and general networking/storage concepts is also recommended. The exam does NOT require ML training/inference skills or deep programming language syntax knowledge. Our practice tests help bridge any knowledge gaps.

Our product delivers 400+ practice questions organized across all four domains with correct weightings. Each question includes detailed explanations that explain the reasoning behind correct and incorrect answers, designed to build the analytical decision-making skills data engineers need daily. You get full-length simulation exams (65 questions, 130 minutes) that replicate the real Pearson VUE testing experience, domain-wise practice modules for focused study on weaker areas, and a downloadable PDF study guide featuring AWS service comparison tables, pipeline architecture diagrams, and key concept summaries for quick reference.

Study time varies by background. Those with 2+ years of data engineering experience typically need 4-6 weeks (60-80 hours). Candidates with AWS experience but new to data engineering may need 8-12 weeks (100-140 hours). We recommend a phased approach: (1) Review the official exam guide and in-scope services list; (2) Complete AWS Skill Builder's DEA-C01 Exam Prep digital courses; (3) Practice hands-on with relevant AWS services; (4) Use our practice tests for domain-wise assessment; (5) Take full-length simulation exams; (6) Focus final review on weak domains identified through practice test performance.

Yes — Data Ingestion and Transformation is the highest-weighted domain at 34%, and AWS Glue is at the center of it. You need to understand Glue ETL jobs (Python Shell, Spark), Glue crawlers and the Data Catalog, Glue Studio for visual ETL, and Glue DataBrew for data preparation. Beyond Glue, expect questions on Kinesis for streaming ingestion, DMS for database migration, Step Functions for pipeline orchestration, and best practices for data format selection (Parquet, ORC, Avro) and partitioning strategies. Knowing how to build end-to-end data pipelines is essential for exam success.

The DEA-C01 is one of AWS's newest Associate certifications and addresses the rapidly growing demand for skilled data engineers. As organizations increasingly build data-driven applications and AI/ML pipelines, certified data engineers are critical for designing and operating the underlying data infrastructure. This certification positions you for roles such as Data Engineer, Analytics Engineer, Data Platform Architect, and Big Data Engineer. It also serves as a natural stepping stone to the AWS Certified Data Analytics - Specialty (DAS-C01) or Machine Learning Engineer - Associate (MLA-C01) for those advancing in the data and AI space.