MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is failing with an OutOfMemory error. The data scientist wants to minimize cost while resolving the issue. Which action should the data scientist take?
⚠ Common exam trap
Many candidates assume reducing the batch size (Option C) is the standard fix for memory issues, but they overlook that the 'File' input mode's full dataset download is the primary cause, and 'Pipe' mode directly addresses this without additional cost.
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
✓
Change the input mode from 'File' to 'Pipe'.
The OutOfMemory error occurs because the 'File' input mode downloads the entire training dataset to the instance's local storage before training begins, consuming significant memory. Switching to 'Pipe' mode streams data directly from S3 to the training algorithm, reducing memory footprint and avoiding the need for larger instances. This minimizes cost by using the existing instance type while resolving the memory issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the instance type to one with more memory.
Why it's wrong here
Increases cost unnecessarily.
- ✗
Use the 'auto' setting for the input mode.
Why it's wrong here
'auto' defaults to File mode if possible, not reducing memory.
- ✗
Reduce the batch size hyperparameter.
Why it's wrong here
May help but not as effective as Pipe mode and could impact model quality.
- ✓
Change the input mode from 'File' to 'Pipe'.
Why this is correct
Pipe mode streams data, reducing memory footprint.
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
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.