MLS-C01 Data Engineering Practice Question
A data engineer is designing a data lake on Amazon S3. The data comes from various sources, including IoT devices, web logs, and transactional databases. The engineer needs to organize the data in a way that supports efficient querying using Amazon Athena and allows for easy management of access permissions. Which S3 bucket structure is the most appropriate?
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
✓
Use a prefix structure like s3://bucket/source/year/month/day/.
Partitioning by source, year, month, day allows Athena to prune partitions, reducing scan costs and improving performance. Option A is wrong because storing all data in a flat structure forces full scans. Option C is wrong because prefix-based access controls can be applied at the source level within the partitioned structure. Option D is wrong because using date as the first partition level is less intuitive for managing permissions by source.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store all data in a single prefix without any partitioning.
Why it's wrong here
Without partitioning, Athena would scan all objects in every query, leading to high costs and poor performance.
- ✓
Use a prefix structure like s3://bucket/source/year/month/day/.
Why this is correct
This structure enables partition pruning by source and time, optimizing Athena queries and allowing granular access control at the source level.
- ✗
Store all data in separate S3 buckets for each source and date.
Why it's wrong here
Creating many buckets increases management overhead; a single bucket with partitioning is recommended.
- ✗
Use a prefix structure like s3://bucket/date/source/.
Why it's wrong here
While still partitioned, the order of keys makes it harder to apply access controls by source.
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.