MLS-C01 Data Engineering Practice Question
A company stores IoT sensor data in Amazon S3 and uses Amazon Athena for ad-hoc queries. The data is partitioned by date, but queries are still slow and expensive. Which TWO actions can improve query performance and reduce cost? (Choose TWO.)
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 S3 lifecycle policies to compact small files into larger ones
Compacts small files into larger ones, reducing the number of objects and minimizing metadata overhead, which improves query performance. Option B converts data from CSV to Parquet, a columnar format that reduces the amount of data scanned by Athena, lowering cost and speeding up queries. Option C (disabling encryption) does not affect performance and is not recommended. Option D (using Glue) is a different service and not a direct improvement for Athena queries. Option E (increasing partitions to hour-level) can create many small files, degrading performance.
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 S3 lifecycle policies to compact small files into larger ones
Why this is correct
Fewer, larger files reduce the overhead of opening many files in Athena.
- ✓
Convert the data from CSV to Parquet format
Why this is correct
Parquet is columnar and compressed, reducing the amount of data scanned by Athena.
- ✗
Disable server-side encryption on the S3 bucket
Why it's wrong here
Encryption has negligible impact on query performance.
- ✗
Use AWS Glue instead of Athena for querying
Why it's wrong here
Glue is for ETL, not interactive queries.
- ✗
Increase the number of partitions to hour-level granularity
Why it's wrong here
More partitions can lead to many small files, degrading performance.
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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