MLS-C01 Data Engineering Practice Question
A company is running a machine learning training job on Amazon SageMaker that reads training data from an S3 bucket. The job fails intermittently with an S3 throttling error. The data is partitioned across thousands of small files (average 100 KB). Which strategy is MOST effective to resolve the throttling issue?
⚠ Common exam trap
Test-takers frequently confuse network-level optimizations (Transfer Acceleration) or parallelization (more instances) with the fundamental S3 request rate limit, which is a per-prefix throughput constraint, not a bandwidth issue.
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
✓
Combine the small files into larger files (e.g., 100 MB) using a preprocessing step
S3 throttling errors (HTTP 503) occur when many small files cause a high request rate per prefix. By combining thousands of 100 KB files into fewer 100 MB files, you drastically reduce the number of GET requests, staying within S3's 5,500 GET requests per second per prefix limit. This preprocessing step directly addresses the root cause of the throttling without changing the training infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Athena to query the data and output results to a new S3 location
Why it's wrong here
Athena is for querying, not for preparing training data efficiently.
- ✗
Enable S3 Transfer Acceleration on the bucket
Why it's wrong here
Transfer Acceleration improves speed but does not reduce the number of requests.
- ✓
Combine the small files into larger files (e.g., 100 MB) using a preprocessing step
Why this is correct
Larger files reduce the number of GET requests, mitigating throttling.
- ✗
Increase the number of SageMaker training instances to distribute the load
Why it's wrong here
More instances increase concurrent requests, potentially worsening throttling.
Visual reference
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
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.