DEA-C01 Data Store Management Practice Question
A data engineer manages an Amazon S3 data lake with millions of small JSON files. To improve query performance with Amazon Athena, the engineer wants to compact these files into larger Parquet files. The engineer must also minimize ongoing storage costs. Which solution should the engineer implement?
⚠ Common exam trap
The trap here is assuming that S3 Transfer Acceleration or streaming services like Kinesis Data Firehose can optimize existing batch data in S3, when they are designed for different use cases.
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 to read the JSON files, transform them to Parquet, and write the output to a new S3 prefix. Then, configure an S3 Lifecycle rule to expire the original JSON objects after a retention period.
Converting JSON to Parquet with AWS Glue ETL compacts files and enables columnar storage, which improves Athena query performance. Writing to a new prefix and using an S3 Lifecycle rule to expire original JSON files after a retention period reduces storage costs while preserving data integrity. This combination addresses both performance and cost requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an AWS Lambda function that triggers on each S3 PUT event to merge JSON files into larger files.
Why it's wrong here
A Lambda function triggered per PUT event would process only new files, not the existing millions of small files. Additionally, merging JSON files into larger JSON files does not provide the columnar storage benefits of Parquet, so Athena performance may not improve significantly. This approach also adds complexity and potential throttling.
- ✓
Use AWS Glue ETL jobs to read the JSON files, transform them to Parquet, and write the output to a new S3 prefix. Then, configure an S3 Lifecycle rule to expire the original JSON objects after a retention period.
Why this is correct
AWS Glue ETL can efficiently convert JSON to Parquet, reducing file count and improving Athena query performance. Writing to a new prefix preserves the original data until verified. An S3 Lifecycle rule to expire the original JSON objects after a retention period reduces storage costs without immediate data loss. This approach is scalable and aligns with best practices for data lake optimization.
- ✗
Use Amazon Kinesis Data Firehose to stream the JSON files into Parquet format in real time.
Why it's wrong here
Kinesis Data Firehose can convert incoming streaming data to Parquet, but the data is already stored in S3 as historical JSON files. Firehose is designed for streaming ingestion, not for batch transformation of existing S3 objects. This solution would not process the existing small files and would not reduce storage costs.
- ✗
Enable S3 Transfer Acceleration on the bucket to speed up read operations for Athena queries.
Why it's wrong here
S3 Transfer Acceleration speeds up uploads and downloads over long distances by using AWS edge locations, but it does not improve Athena query performance on small files. Athena performance is primarily affected by file size, format, and partitioning. This option does not address the root cause of many small files, nor does it reduce storage costs.
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 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
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