DEA-C01 Data Store Management Practice Question
A media company stores millions of thumbnail images in an Amazon S3 bucket. Analysts run ad hoc queries against the image metadata, which is kept as JSON objects in the same bucket. Query latency is unpredictable and costs are rising because Athena scans large volumes of JSON for every query. The team wants faster queries and lower scan cost while keeping the data in S3 and queryable with SQL. Which change should the data engineer make?
⚠ Common exam trap
The trap here is assuming that cheaper S3 storage tiers or higher query limits reduce Athena scan cost, when the real lever is the data format and partitioning.
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 to crawl the metadata, convert it to Apache Parquet partitioned by date, and register the table in the Data Catalog for Athena queries.
The root cause is that Athena must read entire JSON objects and every partition for each query. Converting metadata to columnar Parquet and partitioning by date lets the engine read only needed columns and folders, cutting scanned bytes and latency. Catalog registration keeps the data in S3 and preserves SQL access, which matches all stated constraints.
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 to crawl the metadata, convert it to Apache Parquet partitioned by date, and register the table in the Data Catalog for Athena queries.
Why this is correct
Converting JSON metadata to columnar Parquet lets Athena read only the referenced columns and benefits from compression, while date partitioning restricts each query to relevant folders. Registering the table in the Data Catalog makes it directly queryable, reducing scanned bytes and cost while keeping the data in S3.
- ✗
Enable S3 Intelligent-Tiering on the bucket to automatically move infrequently accessed metadata objects to cheaper storage.
Why it's wrong here
Intelligent-Tiering lowers storage charges by moving objects between access tiers, but it does not change the format Athena reads. Each query still scans the full JSON payload, so scan cost and query latency remain high, which does not satisfy the performance and cost goals.
- ✗
Move the metadata into an Amazon DynamoDB table and have analysts query it with PartiQL.
Why it's wrong here
DynamoDB can store metadata and PartiQL offers SQL-like access, but this moves data out of S3 and changes the query platform. The requirement is to keep data in S3 and query with SQL through the existing lake approach, so relocating to DynamoDB does not meet the stated design.
- ✗
Increase the Athena workgroup data usage control limit so queries can scan more data without failing.
Why it's wrong here
Raising the per-query data usage control allows larger scans but does not make them cheaper or faster; it simply permits more bytes to be billed. The underlying problem of scanning verbose JSON remains, so this change worsens cost rather than solving the performance issue.
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
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