MLS-C01 Data Engineering Practice Question
A company uses AWS Glue Data Catalog to manage metadata for its data lake on Amazon S3. The data lake contains terabytes of data in CSV format. The data engineering team wants to improve query performance in Amazon Athena and reduce costs. Which actions should the team take? (Select THREE.)
⚠ Common exam trap
Test-takers frequently think simplifying queries (views) or switching to another text format (JSON) improves performance, but only compression, partitioning, and columnar formats reduce the amount of data scanned, which is the key to Athena cost and speed 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
✓
Compress the data using Snappy or GZIP.
Compressing CSV data with Snappy or GZIP reduces the amount of data scanned by Athena, directly lowering query costs (Athena charges per TB scanned). Snappy offers faster decompression for better query performance, while GZIP provides higher compression ratios. Both formats are natively supported by Athena and reduce I/O from S3.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create views in Athena to simplify queries.
Why it's wrong here
Views do not improve performance or cost.
- ✓
Compress the data using Snappy or GZIP.
Why this is correct
Compression reduces storage and data scanned.
- ✓
Partition the data by commonly filtered columns.
Why this is correct
Partition pruning limits scanned data.
- ✓
Convert the data to Parquet format.
Why this is correct
Parquet is columnar and reduces I/O.
- ✗
Convert the data to JSON format.
Why it's wrong here
JSON is not columnar and may increase data size.
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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