MLS-C01 Question 317
Single answerYou are building a machine learning workflow using Amazon SageMaker, which involves hosting a model endpoint for real-time inference. During the deployment, you receive an error indicating you have exceeded a service quota. What is the most appropriate action to resolve this issue?
- A
Request a service quota increase for the specific resource in the Service Quotas console.
- B
Upgrade your AWS account to Enterprise Support to increase all service quotas automatically.
- C
Use the AWS CLI to force the deployment, overriding the service quota.
- D
Reduce the number of endpoints you are deploying and retry the operation.
Show answer and explanation
Correct answer: A
Explanation
AWS enforces service quotas to ensure efficient resource allocation and prevent abuse. If you exceed a quota, the recommended approach is to request a service quota increase using the Service Quotas console or by contacting AWS Support. This allows you to scale your resources as needed while following best practices and avoiding temporary workarounds.
- A. Correct.
This is the correct action. AWS service quotas are adjustable, and you can request an increase for a specific resource through the Service Quotas console or AWS Support.
- B. Incorrect.
Upgrading to Enterprise Support does not automatically increase service quotas. Service quota increases must be requested explicitly.
- C. Incorrect.
There is no functionality in the AWS CLI to override service quotas. Service quotas are enforced by AWS and cannot be bypassed without a formal increase request.
- D. Incorrect.
While reducing the number of endpoints might temporarily resolve the issue, it does not address the root cause. Requesting a service quota increase is the proper long-term solution.