hardMultiple ChoiceObjective-mapped
Resolving SageMaker ResourceLimitExceeded Errors via Quota Increase
Exhibit
Refer to the exhibit. Error log from SageMaker endpoint creation: ``` ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateEndpointConfig operation: The account-level service limit for 'ml.c5.large for real-time endpoints' is 5. You have requested 6 instances. Please use AWS Service Quotas to request an increase. ```
A data scientist is trying to create a SageMaker endpoint configuration with 6 instances of ml.c5.large for a production variant. The creation fails with the error shown in the exhibit. Which action should the data scientist take to resolve this issue?
Quick Answer
An error when requesting 6 instances of a given type for an endpoint configuration, without any indication the instance type itself is invalid or unavailable, is characteristic of hitting an account-level service quota, since AWS caps the number of instances of each type you can provision per region by default as a safeguard, and those defaults are often lower than what a production deployment actually needs. The fix isn't to change the instance type, reduce the instance count, or redesign the deployment, since the team has a legitimate need for 6 instances of ml.c5.large; it's to raise the ceiling itself by requesting a service quota increase through the AWS Service Quotas console, which is the standard, supported path for unlocking higher resource limits without any workaround. This is different from a permissions or configuration error, which would typically reference IAM policies or invalid parameters rather than a limit being exceeded, so the specific nature of the error is what points toward a quota issue rather than something in the endpoint configuration itself. Whenever a deployment fails specifically because a requested resource count or size exceeds what the account is currently allowed to provision, and the requested resources are otherwise valid, look for a service quota increase as the resolution rather than any change to the deployment itself.
⚠ Common exam trap
Many exam-takers confuse service quotas with resource availability, thinking that deleting unused endpoints or splitting configurations will free up capacity, when in fact the quota is a hard limit that must be explicitly 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 quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console.
The error indicates that the requested number of instances exceeds the service quota for ml.c5.large for real-time endpoints. AWS enforces default limits on instance counts per instance type per region. Requesting a quota increase via the Service Quotas console is the correct action to raise the limit and allow the deployment of 6 instances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create two separate endpoint configurations, each with 3 instances, and distribute traffic between them.
Why it's wrong here
The quota applies to the sum of instances across all endpoints; this would still exceed the limit.
- ✓
Request a service quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console.
Why this is correct
Increasing the quota allows provisioning the requested number of instances.
- ✗
Use a different instance type, such as ml.m5.large, which has a higher limit.
Why it's wrong here
Changing instance type may not solve the quota issue; the new type might also have a limit.
- ✗
Delete unused endpoints to free up resources.
Why it's wrong here
The error is about a service limit, not resource availability; deleting endpoints does not increase the quota.
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Variation 1. A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?
medium- ✓ A.The account has a limit of 0 for ml.p3.2xlarge instances.
- B.The volume size is too small for training.
- C.The Preprocess step did not complete successfully.
- D.The training image is not accessible.
Why A: The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.