DEA-C01 Data Ingestion and Transformation Practice Question
A company stores IoT sensor data in S3 as JSON files. They need to convert the data to Parquet format for efficient querying with Amazon Athena. Which AWS service can perform this transformation with minimal effort?
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 can easily convert JSON to Parquet with built-in transforms. Option A is wrong because Kinesis Data Firehose is for streaming data ingestion, not batch transformations. Option B is wrong because Amazon Athena is a query engine, not a transformation service. Option D is wrong because AWS Lambda is for small, event-driven transformations and is not ideal for large-scale batch conversion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Kinesis Data Firehose
Why it's wrong here
Firehose performs record-by-record conversion using AWS Glue schema, but it ingests streaming data, not existing S3 JSON objects already at rest. It is tempting because Firehose does convert JSON to Parquet, which is correct for live streaming pipelines rather than batch files in S3.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data in place; it reads JSON and Parquet but does not rewrite existing S3 objects into Parquet. It is tempting because Athena benefits from Parquet and integrates with Glue, yet the transformation itself requires a separate ETL service such as AWS Glue.
- ✓
AWS Glue ETL job
Why this is correct
AWS Glue ETL jobs read JSON from S3, apply schema and transformation logic, and write Parquet back to S3 with minimal coding via the visual editor or generated scripts. This satisfies the conversion requirement while remaining serverless and low-effort for Athena querying.
- ✗
AWS Lambda
Why it's wrong here
Lambda runs custom code, so converting JSON to Parquet requires writing and maintaining a script, not minimal effort. It is tempting because Lambda is serverless and event-driven, making it a natural fit for lightweight, bespoke transformations rather than a managed ETL conversion.
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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JA
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.