The correct action is to request a service quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console. This is necessary because SageMaker enforces default instance quota limits per instance type per region, and the error indicates that the requested 6 instances exceed the current limit for ml.c5.large in real-time endpoint configurations. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to handle SageMaker endpoint instance quota limits when deploying production variants, often appearing as a trap where candidates mistakenly try to change instance types or modify endpoint configurations instead of addressing the underlying quota. A common memory tip is to think of quotas as regional ceilings—no matter how you configure the endpoint, you cannot exceed the allowed instance count without first raising the ceiling through Service Quotas. Remember: when the error says “limit exceeded,” always check the quota before touching the endpoint.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Create two separate endpoint configurations, each with 3 instances, and distribute traffic between them.
Why wrong: The quota applies to the sum of instances across all endpoints; this would still exceed the limit.
B
Request a service quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console.
Increasing the quota allows provisioning the requested number of instances.
C
Use a different instance type, such as ml.m5.large, which has a higher limit.
Why wrong: Changing instance type may not solve the quota issue; the new type might also have a limit.
D
Delete unused endpoints to free up resources.
Why wrong: The error is about a service limit, not resource availability; deleting endpoints does not increase the quota.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Service quotas for SageMaker are per-instance-type, per-region, and apply to both real-time and serverless endpoints. The default quota for ml.c5.large is typically 5 instances per region, which explains the failure when requesting 6. The quota can be increased by submitting a request to AWS Service Quotas, which is a regional, account-level adjustment that does not require deleting existing resources.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.