MLS-C01 Data Engineering Practice Question
A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?
⚠ Common exam trap
Many candidates assume compression alone (gzip) is sufficient to improve performance, but they overlook that columnar formats like Parquet provide both compression and column pruning, which is the key to reducing scanned data and cost in Athena.
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
Converting JSON data to Parquet format significantly improves Athena query performance and reduces cost. Parquet is a columnar storage format that allows Athena to scan only the columns needed for a query, drastically reducing the amount of data read from S3. This minimizes I/O and compute costs, as Athena charges based on the amount of data scanned. In contrast, JSON is row-based and requires scanning entire files even for queries that only touch a few columns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compress the JSON files using gzip
Why it's wrong here
Compression reduces storage but Athena still scans entire rows.
- ✗
Partition the data by date
Why it's wrong here
Partitioning helps but is less effective than columnar format for reducing scan size.
- ✓
Convert the data to Parquet format
Why this is correct
Parquet is columnar, reducing scanned data and improving performance.
- ✗
Increase the number of Athena workers
Why it's wrong here
Athena is serverless; you cannot configure workers.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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