MLS-C01 Data Engineering Practice Question
A data engineer is designing a data lake on Amazon S3 that must support both batch and streaming analytics. The data comes in Parquet format and needs to be queryable by Amazon Athena. Which partitioning strategy will optimize query performance and reduce costs?
⚠ Common exam trap
AWS often tests the misconception that high-cardinality partitions (like device_id) improve query performance, but in Athena and Presto, they actually degrade performance due to excessive partition metadata and small file overhead, whereas coarse-grained time partitions are the recommended pattern.
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 by date and hour for time-based queries
Partitioning by date and hour is optimal for time-series data in Parquet format queried by Athena because it leverages Hive-style partitioning to prune partitions during query execution, drastically reducing the amount of data scanned. This minimizes Athena's cost (which is based on data scanned) and improves query performance by limiting I/O to only the relevant partitions. Parquet's columnar storage further reduces scan volume when queries select only specific columns, making this combination highly efficient for both batch and streaming ingestion patterns.
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 by date and hour for time-based queries
Why this is correct
Common query patterns are time-filtered; this reduces data scanned.
- ✗
Store data as CSV without partitioning for simplicity
Why it's wrong here
CSV is not columnar and lacks compression, increasing scan costs.
- ✗
Partition by device_id for granular access
Why it's wrong here
High cardinality leads to many small partitions and poor performance.
- ✗
Use a single partition for all data to simplify management
Why it's wrong here
No partition pruning will occur, causing full table scans.
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.