MLS-C01 Data Engineering Practice Question
A company has a large number of small CSV files (hundreds of thousands) in an S3 bucket. A data engineer needs to run a SQL query on this data using Amazon Athena. The queries are currently slow and expensive. Which two actions will improve query performance and reduce cost?
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
✓
Partition the data by a commonly filtered column (e.g., date).
The correct answers are C and E. Partitioning the data by a commonly filtered column (e.g., date) reduces the amount of data scanned by Athena, improving performance and cost. Converting the data to Parquet or ORC columnar format further reduces the data scanned and improves compression and query speed. Option B (gzip compression) helps reduce storage and scan volume but is less impactful than partitioning and columnar format. Option A (increasing S3 request rate) does not directly improve Athena query performance. Option D (splitting into smaller files) can increase the overhead of reading many small files, potentially hurting 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.
- ✗
Increase the S3 request rate per prefix to improve read throughput.
Why it's wrong here
Athena handles S3 request rates; this is not a user-configurable action.
- ✗
Compress the CSV files using gzip.
Why it's wrong here
Compression reduces storage size but Athena still scans the entire uncompressed data.
- ✓
Partition the data by a commonly filtered column (e.g., date).
Why this is correct
Partitioning limits the data scanned per query, improving performance and reducing cost.
- ✗
Increase the number of partitions by splitting files into smaller ones.
Why it's wrong here
Too many small files can increase overhead and degrade performance.
- ✓
Convert the data to Parquet or ORC columnar format.
Why this is correct
Columnar formats reduce scanned data and improve compression and query 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
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.