DEA-C01 Data Ingestion and Transformation Practice Question
A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?
⚠ Common exam trap
DEA-C01 often tests Glue service selection by describing a transformation task — candidates confuse Glue Workflow (orchestration) or Glue Crawler (cataloging) with the actual ETL engine, picking a service that schedules or catalogs rather than transforms.
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
✓
AWS Glue ETL job
AWS Glue ETL jobs are purpose-built for extracting data from sources like S3, applying transformations (including schema mapping and format conversion), and loading the results into targets like Redshift. For a simple CSV-to-Parquet conversion with schema mapping and no joins, a Glue ETL job using the built-in transforms or a Spark script is the correct tool. It handles the conversion and load in a single managed job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue ETL job
Why this is correct
AWS Glue ETL jobs handle schema mapping and format conversion natively, reading CSV from S3 and writing Parquet to Redshift through the Glue Data Catalog and JDBC/Redshift connections. No joins are required, so a straightforward ETL job satisfies the daily conversion and load requirement.
- ✗
AWS Glue DataBrew
Why it's wrong here
DataBrew targets visual, interactive data preparation by analysts rather than scheduled ETL jobs that write Parquet into Redshift. Glue ETL jobs with a crawler and transform script handle this repeatable CSV-to-Parquet conversion and load pattern.
- ✗
AWS Glue Workflow
Why it's wrong here
Glue Workflow orchestrates and schedules multiple crawlers and jobs as a dependency graph; it does not itself perform the CSV-to-Parquet transformation or Redshift load. A single Glue ETL job suffices here, so the workflow adds orchestration without providing the required conversion logic.
- ✗
AWS Glue Crawler
Why it's wrong here
Crawlers populate the Data Catalog with table metadata; they neither transform CSV to Parquet nor load Redshift. This task needs a Glue ETL job with a write-to-Redshift connection. Crawlers are the right choice when you must discover schemas and partitions across unknown S3 data before querying it in Athena.
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.