MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses Amazon SageMaker to train a model. The training job fails with an 'OutOfMemory' error. The training data is stored in S3 and the instance type is ml.m5.xlarge. What is the most efficient way to resolve this issue?
⚠ Common exam trap
Many exam-takers choose 'Reduce the batch size' (Option B) as a quick fix, but the question asks for the 'most efficient' solution—changing instance type requires no code changes and is faster to implement, whereas batch size reduction requires debugging and retesting the training script.
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 larger instance type, such as ml.m5.2xlarge
The 'OutOfMemory' error indicates that the ml.m5.xlarge instance (4 vCPUs, 16 GiB memory) does not have enough RAM to hold the training data and model during processing. Upgrading to ml.m5.2xlarge (8 vCPUs, 32 GiB memory) directly increases available memory, resolving the issue without requiring code changes or architectural modifications. This is the most efficient solution because it requires no script alterations and leverages SageMaker's built-in instance scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable managed spot training
Why it's wrong here
Spot instances do not affect memory.
- ✗
Reduce the batch size in the training script
Why it's wrong here
This may help but is not the most efficient solution.
- ✗
Increase the number of instances using distributed training
Why it's wrong here
Distributed training does not increase memory per instance.
- ✓
Use a larger instance type, such as ml.m5.2xlarge
Why this is correct
Larger instance provides more memory.
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
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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.