MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a neural network. The training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.8xlarge for training job usage is 0.' What is the most likely cause and solution?
⚠ Common exam trap
Many exam-takers confuse a service limit error with instance availability or spot instance issues, but the specific phrase 'limit is 0' directly points to an unrequested quota increase, not a regional or pricing model problem.
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 not requested a limit increase for ml.p3.8xlarge; submit a limit increase request via AWS Support.
The error message explicitly states that the account-level service limit for ml.p3.8xlarge for training job usage is 0, which means the account has not been granted any capacity for that instance type. AWS enforces service quotas (limits) per account per region, and for GPU-intensive instances like ml.p3.8xlarge, the default limit is often 0 unless a limit increase request has been submitted and approved. Therefore, the correct solution is to request a limit increase via AWS Support.
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 job is using spot instances; switch to on-demand instances.
Why it's wrong here
No indication of spot instance usage.
- ✗
The instance type is not available in the current region; switch to a different region.
Why it's wrong here
The error is about service limits, not availability.
- ✓
The account has not requested a limit increase for ml.p3.8xlarge; submit a limit increase request via AWS Support.
Why this is correct
ResourceLimitExceeded indicates the current limit is zero; a limit increase is needed.
- ✗
The instance type is too large; use a smaller instance type like ml.m5.large.
Why it's wrong here
The error is about limit, not size; smaller instance may also have zero limit.
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