MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a deep learning model on Amazon SageMaker using the built-in Object Detection algorithm. The training job is failing with a 'ResourceLimitExceeded' error when trying to launch multiple GPU instances. Which of the following 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 the limit for the number of GPU instances in the current AWS Region.
The 'ResourceLimitExceeded' error indicates that the account has exceeded its service limit for the number of GPU instances (or any SageMaker training instances) in the current AWS Region. Option A is incorrect because a syntax error would result in a different error (e.g., 'ModuleNotFoundError' or 'SyntaxError'). Option B is incorrect because the dataset size does not directly cause a resource limit error; it might cause out-of-memory issues but not a resource limit exceeded. Option D is incorrect because insufficient S3 bucket permissions would cause an 'AccessDenied' error, not 'ResourceLimitExceeded'.
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 training script has a syntax error.
Why it's wrong here
Syntax errors cause AlgorithmError, not resource limit errors.
- ✗
The dataset is too large for the selected instance type.
Why it's wrong here
SageMaker does not limit dataset size; error is about resource limits.
- ✓
The account has reached the limit for the number of GPU instances in the current AWS Region.
Why this is correct
ResourceLimitExceeded indicates service limit reached; contact AWS to increase limits.
- ✗
The S3 bucket containing the training data has insufficient permissions.
Why it's wrong here
Permissions issues cause AccessDenied errors, not ResourceLimitExceeded.
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.