DEA-C01 AWS Glue ETL Practice Question
A data engineer needs to set up a data pipeline that ingests CSV files from an S3 bucket, transforms them using AWS Glue, and loads the results into Amazon Redshift. The pipeline must handle schema evolution and data quality checks. Which combination of services is most appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue ETL jobs with Glue DataBrew for data quality and write to Redshift
AWS Glue ETL jobs can handle schema evolution through the use of Glue DynamicFrames, and Glue DataBrew provides built-in data quality checks (profiling, validation) that integrate seamlessly. Option A is incorrect because Lambda has timeout and memory limits, making it unsuitable for large-scale transformations. Option B is incorrect because Athena cannot write directly to Redshift; CTAS only writes to S3. Option C is incorrect because Kinesis Data Firehose is designed for streaming data, not batch CSV ingestion from S3.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use S3 Events to trigger an AWS Lambda function that writes directly to Redshift
Why it's wrong here
Lambda is not designed for heavy ETL and lacks schema evolution handling.
- ✗
Use Amazon Athena to query data in S3 and insert results into Redshift via CTAS
Why it's wrong here
Athena cannot write to Redshift directly.
- ✗
Use Amazon Kinesis Data Firehose to transform and load data into Redshift
Why it's wrong here
Firehose is for streaming, not batch processing with complex transforms.
- ✓
Use AWS Glue ETL jobs with Glue DataBrew for data quality and write to Redshift
Why this is correct
Glue supports schema evolution and DataBrew provides data quality checks.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.