MLS-C01 Data Engineering Practice Question
An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to an Amazon S3 bucket. The data is then queried using Amazon Athena. The marketing team wants to run daily reports that aggregate click events by product ID. However, the reports are slow because Athena scans the entire dataset each time. The data is partitioned by date (e.g., s3://bucket/clickstream/2023/01/01/). The product ID is a column within the data. The data engineering team wants to improve query performance without moving the data to another service. Which approach should the team take?
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
✓
Repartition the data by product ID in addition to date
Repartition the data by product ID in addition to date. This adds a partition level for product ID, so queries that filter on product ID will only scan the relevant partitions. Option A (convert to Parquet) reduces data scanned due to columnar storage and compression, but without partition pruning on product ID, Athena would still scan all partitions for each query. Option B (Redshift Spectrum) would still require scanning data, and involves additional service complexity. Option C (create a view) does not change physical storage; it only provides a logical filter, but Athena still scans all underlying data. Therefore, repartitioning by product ID provides the most direct improvement for queries filtering by product ID.
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 from JSON to Parquet format
Why it's wrong here
Parquet reduces scan size but still scans all partitions if no partition pruning.
- ✗
Use Amazon Redshift Spectrum to query the data
Why it's wrong here
Spectrum still scans the data in S3; partitioning helps but not addressed.
- ✗
Create a view in Athena that filters by product ID
Why it's wrong here
A view does not change underlying data organization; still full scan.
- ✓
Repartition the data by product ID in addition to date
Why this is correct
Partitioning by product ID allows Athena to skip irrelevant partitions.
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.