Amazon
AWS Certified Data Engineer - Associate (DEA-C01) practice questions
- Questions
- 100
- Exam length
- 130 min
- Passing score
- 720
- Updated
- Instant feedback per question
- Explanation for every option
- Free, no signup
Domain coverage 4 domains
- 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
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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?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?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?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?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?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?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?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?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?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?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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Frequently asked questions
How does DEA-C01 differ from the Solutions Architect Associate (SAA-C03)?
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.
What are the most important AWS services for the DEA-C01 exam?
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.
What prerequisite knowledge is recommended for the DEA-C01 exam?
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.
What does your DEA-C01 practice test product include?
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.
How much study time is recommended for the DEA-C01 exam?
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.
Are AWS Glue and ETL pipelines a major focus of the exam?
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.
What career value does the AWS Data Engineer Associate certification provide?
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.
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