DEA-C01 Data Store Management Practice Question
A media company ingests thousands of small JSON files per hour into an Amazon S3 bucket. A data engineer needs to convert these files into a compact, columnar format for efficient querying with Amazon Athena. The engineer wants to minimize storage costs and improve query performance. Which approach should the engineer take?
⚠ Common exam trap
The trap here is assuming that streaming services like Kinesis Data Firehose or serverless functions like Lambda are the best fit for batch conversion of existing small files, when a managed ETL service like AWS Glue is more 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 to read the JSON files, convert them to Apache Parquet, and write the output to a new S3 prefix partitioned by date.
AWS Glue ETL is a fully managed extract, transform, and load service that can efficiently process large volumes of data. It can read JSON from S3, convert to Parquet, and write partitioned output. This reduces storage costs due to Parquet's compression and columnar format, and improves Athena query performance by reducing data scanned. Partitioning by date further optimizes queries.
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 AWS Glue ETL to read the JSON files, convert them to Apache Parquet, and write the output to a new S3 prefix partitioned by date.
Why this is correct
AWS Glue ETL can read JSON, transform to Parquet, and write partitioned data to S3. Parquet is columnar, reducing storage and improving Athena query performance. Partitioning by date further reduces data scanned. This directly addresses the need for compact columnar format and cost efficiency.
- ✗
Use Amazon Athena to create a new table as Parquet using CREATE TABLE AS SELECT (CTAS) from the JSON table.
Why it's wrong here
Athena CTAS can convert JSON to Parquet and partition the output, but it is best for one-time or periodic conversions. For continuous ingestion of thousands of small files, CTAS would need to be run repeatedly, potentially causing high costs and latency. It is not the most efficient for ongoing ETL.
- ✗
Use Amazon Kinesis Data Firehose to convert JSON to Parquet and deliver to S3.
Why it's wrong here
Kinesis Data Firehose can convert JSON to Parquet using AWS Lambda, but it is designed for streaming data and may not efficiently handle thousands of small files already in S3. It also lacks the flexibility to partition by date without custom logic. This approach is more complex and may not optimize existing files.
- ✗
Use AWS Lambda to read each JSON file, convert to Parquet, and write back to S3.
Why it's wrong here
AWS Lambda can process files individually, but handling thousands of small files per hour would require many concurrent executions, increasing cost and complexity. Lambda has limited memory and time, making it less suitable for large-scale ETL. It also lacks built-in partitioning and cataloging capabilities.
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.