MLS-C01 Data Engineering Practice Question
Network Topology
A data engineer is investigating why an Athena query against the my-data-lake bucket is slow. The query filters on year, month, and day. The exhibit shows the metadata of one Parquet file. What is the MOST likely cause of the slow query?
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
✓
The file is too large, causing Athena to process it in a single task
The Parquet file is 1 GB in size, which is too large for efficient processing in Athena. Athena splits data into tasks for parallel execution, but a single large file cannot be split, causing the query to run slowly. Partitioning on year, month, and day is already applied and is not the issue. The other options are incorrect: version ID null is irrelevant, the query does use partition columns, and standard storage is faster than Glacier.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The version ID is null, causing data inconsistency
Why it's wrong here
Null version ID means versioning is not enabled; this does not affect performance.
- ✓
The file is too large, causing Athena to process it in a single task
Why this is correct
Large files limit parallelism; Athena works best with files 128-512 MB.
- ✗
The partition columns are not being used in the query
Why it's wrong here
The query filters on year, month, day, so partition pruning should occur.
- ✗
The storage class is STANDARD, which is slower than GLACIER
Why it's wrong here
STANDARD is the fastest storage class for frequent access.
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.