MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is using Amazon Athena to query a partitioned dataset in S3. They notice that queries are scanning more data than expected. Which step should they take during exploratory data analysis to optimize 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
✓
Check the partition metadata to ensure queries are pruning partitions.
Checking partition metadata (e.g., using SHOW PARTITIONS or querying information_schema) ensures that queries are applying partition pruning, which reduces the amount of data scanned. Option A is incorrect: converting to Parquet improves columnar scan efficiency but does not directly address partition misuse. Option B is incorrect: S3 Select filters data at the object level, but Athena already pushes down filters; this does not fix a lack of partition pruning. Option C is incorrect: increasing workers improves parallelism but does not reduce scanned data if partitions are not pruned.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the data to Parquet format.
Why it's wrong here
Parquet improves columnar scanning but does not fix partition issues.
- ✗
Use S3 Select to filter data before querying.
Why it's wrong here
S3 Select is for simple filtering, not partition optimization.
- ✗
Increase the number of workers in Athena.
Why it's wrong here
More workers do not reduce scanned data if partitions are not used.
- ✓
Check the partition metadata to ensure queries are pruning partitions.
Why this is correct
Verifying partition structure ensures efficient partition pruning.
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 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.