MLS-C01 Data Engineering Practice Question
A data scientist needs to query a 2 TB dataset stored in Amazon S3 using Amazon Athena. The data is in CSV format and is used for exploratory analysis. Queries are currently slow and expensive. Which action will improve query performance and reduce cost?
⚠ Common exam trap
Candidates often think increasing compute resources (Option B) or adding indexes (Option D) works in Athena as it does in traditional databases, but Athena is serverless and index-free, relying on storage format and partitioning for optimization.
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
✓
Convert the data to Parquet format and partition by a commonly filtered column.
Converting CSV data to Parquet (a columnar storage format) significantly reduces the amount of data scanned by Athena, as only the columns needed for the query are read. Partitioning by a commonly filtered column (e.g., date or region) further limits the data scanned to only relevant partitions, directly reducing both query cost (Athena charges per TB scanned) and query execution time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the data to JSON format to improve compression.
Why it's wrong here
JSON is not as efficient as columnar formats like Parquet for analytical queries.
- ✗
Increase the number of workers in the Athena query engine.
Why it's wrong here
Athena is serverless and does not have configurable workers.
- ✓
Convert the data to Parquet format and partition by a commonly filtered column.
Why this is correct
Parquet reduces data scanned due to columnar storage, and partitioning limits scan range.
- ✗
Create a composite index on the data using Athena's index feature.
Why it's wrong here
Athena does not support indexes.
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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