Question 1,602 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train a random forest model. The dataset has 100 features and 1 million rows. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause?
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
✓
The account has reached its limit on the number of SageMaker training instances.
The 'ResourceLimitExceeded' error indicates that the account has reached its limit on the number of SageMaker training instances or vCPUs. Option A (S3 bucket region) would cause a different error, not a resource limit. Option B (GPU memory) is unlikely because random forest models typically use CPU instances. Option C (wrong algorithm) would result in an algorithm-specific error, not a resource limit. Option D correctly identifies that the account limit has been exceeded.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The S3 bucket containing the training data is not in the same region.
Why it's wrong here
Cross-region access works; it would not cause this error.
- ✗
The instance type selected does not have enough GPU memory.
Why it's wrong here
Random forest does not use GPU.
- ✗
The wrong algorithm was specified for the training job.
Why it's wrong here
Wrong algorithm would cause a validation error, not resource limit.
- ✓
The account has reached its limit on the number of SageMaker training instances.
Why this is correct
ResourceLimitExceeded indicates a service quota limit.
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: Jun 20, 2026
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