DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform data in Amazon S3 using SQL statements without managing any infrastructure. The transformations are simple projections and filters, and the engineer wants the results written back to S3 in Parquet. Which AWS service should be used?
⚠ Common exam trap
The trap here is equating SQL-on-S3 with Glue or EMR, when Athena is the serverless option purpose-built for running SQL directly against S3 data.
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
✓
Amazon Athena with a CREATE TABLE AS SELECT statement
Amazon Athena is fully serverless and executes SQL directly against S3 data. A CREATE TABLE AS SELECT statement reads the source, applies projections and filters, and writes the result to S3 in a chosen format like Parquet. This satisfies the SQL and no-infrastructure requirements without cluster or job management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Athena with a CREATE TABLE AS SELECT statement
Why this is correct
Athena is serverless and runs standard SQL against data in S3, and CREATE TABLE AS SELECT writes the transformed result back to S3 in a specified format such as Parquet. This matches the requirement for simple SQL transformations with no infrastructure to manage.
- ✗
AWS Glue ETL with a PySpark script
Why it's wrong here
Glue ETL supports SQL through Spark SQL but requires authoring a script and managing job configuration, which is more overhead than needed for simple projections and filters. The requirement is specifically to run SQL without infrastructure management, which a serverless query service handles more directly.
- ✗
Amazon EMR with Hive on a transient cluster
Why it's wrong here
EMR requires provisioning and managing clusters, even transient ones, which contradicts the no-infrastructure requirement. Hive on EMR can run SQL, but the operational overhead of cluster lifecycle management makes it unsuitable for simple projections and filters.
- ✗
Amazon Redshift Spectrum with an external schema
Why it's wrong here
Redshift Spectrum queries external S3 data but requires a Redshift cluster, which is infrastructure to manage. It is designed for joining external data with Redshift tables, not for standalone serverless SQL transformations written back to S3.
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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JA
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.