MLS-C01 Modeling Practice Question
A team is using Amazon SageMaker to train a model. The training job repeatedly fails with a 'ResourceLimitExceeded' error. Which action should the team take to resolve this issue?
⚠ Common exam trap
Candidates often confuse resource limits with performance or cost issues, leading them to choose dataset reduction, Spot Instances, or smaller instance types, when the root cause is a hard AWS service quota that must be increased.
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
✓
Request a service limit increase for SageMaker resources.
The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has reached a service quota for SageMaker resources, such as the number of concurrent training jobs, total instance count, or specific instance types. The correct action is to request a service limit increase via the AWS Service Quotas console or by contacting AWS Support, as this directly addresses the quota cap causing the failure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Request a service limit increase for SageMaker resources.
Why this is correct
ResourceLimitExceeded indicates the account limit has been reached; requesting an increase is the standard resolution.
- ✗
Reduce the size of the training dataset.
Why it's wrong here
The error is not related to dataset size but to the number or type of resources.
- ✗
Switch to using Spot Instances.
Why it's wrong here
Spot Instances have their own limits and may not resolve a general limit exceeded error.
- ✗
Use a different instance type with less memory.
Why it's wrong here
The error is about resource limits, not instance capability.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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