Question 1,479 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a model using Amazon SageMaker with a custom Docker container. The training job fails with an error: 'Resource exhausted: Out of memory'. The training data is stored in S3. What should the data scientist do to resolve this issue?
⚠ Common exam trap
Many exam-takers confuse memory (RAM) with storage (EBS volume) or data loading modes, mistakenly thinking that increasing disk space or changing data ingestion methods will fix an out-of-memory error, when the root cause is insufficient RAM on the compute instance.
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
✓
Increase the instance memory by selecting a larger instance type.
The 'Resource exhausted: Out of memory' error indicates that the training instance's RAM is insufficient for the workload. Selecting a larger instance type with more memory directly addresses the OOM condition by providing additional physical RAM for model parameters, data batches, and intermediate computations. In SageMaker, instance types like ml.p3.2xlarge (61 GB RAM) vs. ml.p3.8xlarge (244 GB RAM) offer different memory capacities, and upgrading resolves memory exhaustion without altering the training logic.
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 memory by selecting a larger instance type.
Why this is correct
Larger instance provides more memory.
- ✗
Increase the EBS volume size attached to the training instance.
Why it's wrong here
EBS size does not affect memory.
- ✗
Use Pipe mode for data loading instead of File mode.
Why it's wrong here
Pipe mode reduces local storage, not memory.
- ✗
Reduce the batch size in the training script.
Why it's wrong here
Reducing batch size may help but instance memory is the root cause.
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 |
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Last reviewed: Jul 4, 2026
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.
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