MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses SageMaker Pipelines to automate model retraining. The pipeline fails intermittently at the Preprocess step with a 'ResourceLimitExceeded' error. The team uses a ml.m5.xlarge instance. 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 the limit for concurrent ml.m5.xlarge instances
The error indicates reaching a service quota. SageMaker has a default limit of concurrent training jobs per account. Option A is correct. Option B would cause different errors. Option C is unrelated. Option D would cause a SageMaker service error, not a resource limit.
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 account has reached the limit for concurrent ml.m5.xlarge instances
Why this is correct
ResourceLimitExceeded typically means hitting a service limit like concurrent instances.
- ✗
The preprocessing script has a memory leak
Why it's wrong here
A memory leak would cause OutOfMemory, not ResourceLimitExceeded.
- ✗
The S3 bucket has insufficient permissions
Why it's wrong here
Permissions issues cause AccessDenied, not ResourceLimitExceeded.
- ✗
The pipeline execution role is missing the PassRole permission
Why it's wrong here
Missing PassRole causes an authorization error, not a resource limit.
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