MLS-C01 Modeling Practice Question
A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?
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 Pipe input mode in the SageMaker estimator.
Pipe input mode streams training data directly from S3 to the algorithm without writing intermediate files to disk, eliminating I/O wait time. Option A (using Amazon EFS) introduces network file system latency, which would not reduce I/O bottleneck. Option B (using Amazon FSx for Lustre) could improve throughput but is more complex to set up and may not be as effective as Pipe mode for streaming. Option C (increasing the number of training instances) might distribute computation but does not directly reduce per-instance I/O bottleneck.
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 EFS as the data source.
Why it's wrong here
EFS adds network latency, not ideal for high throughput.
- ✗
Use Amazon FSx for Lustre as the data source.
Why it's wrong here
FSx is faster but still involves disk writes; Pipe mode avoids disk entirely.
- ✗
Increase the number of training instances.
Why it's wrong here
More instances can parallelize computation but I/O bottleneck persists.
- ✓
Use Pipe input mode in the SageMaker estimator.
Why this is correct
Pipe mode streams data, reducing disk I/O.
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.