MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?
⚠ Common exam trap
AWS often tests the misconception that more partitions always improve performance, but in reality, over-partitioning leads to metastore overhead and small file problems that degrade query performance.
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 frequently filtered column, such as date
Partitioning by a frequently filtered column, such as date, allows Athena to use partition pruning. When a query includes a filter on the partition column, Athena can skip entire directories of data in S3, drastically reducing the amount of data scanned and improving query performance while also lowering cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the data by a frequently filtered column, such as date
Why this is correct
Partition pruning limits scanned data.
- ✗
Use uncompressed CSV files for simplicity
Why it's wrong here
Uncompressed CSV leads to slower performance and higher costs.
- ✗
Partition the data by every column to maximize filtering
Why it's wrong here
Too many partitions cause many small files and overhead.
- ✓
Store data in columnar formats like Parquet or ORC
Why this is correct
Columnar formats reduce read overhead.
- ✓
Compress the data with Snappy or gzip
Why this is correct
Compression reduces storage and I/O.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.