- A
Request a service quota increase for SageMaker training instances.
Increases the maximum number of instances.
- B
Use spot instances for training.
Spot instances can access additional capacity.
- C
Use Amazon EFS for training data.
Why wrong: Storage choice doesn't affect instance limit.
- D
Reduce the size of the training dataset.
Why wrong: Data size not related to instance limit.
- E
Use a smaller instance type.
Why wrong: Smaller instances don't increase limit.
Question 550 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
A company is using Amazon SageMaker to build a custom model. The training job is failing with a 'ResourceLimitExceeded' error. Which TWO actions should the company take to resolve this issue?
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 SageMaker training instances.
Option A is correct because the 'ResourceLimitExceeded' error indicates that the AWS account has reached its service quota for SageMaker training instances. Requesting a service quota increase through the AWS Service Quotas console or API allows the account to launch additional instances of the required type, directly resolving the limit issue.
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.
- ✓
Request a service quota increase for SageMaker training instances.
Why this is correct
Increases the maximum number of instances.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use spot instances for training.
Why this is correct
Spot instances can access additional capacity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon EFS for training data.
Why it's wrong here
Storage choice doesn't affect instance limit.
- ✗
Reduce the size of the training dataset.
Why it's wrong here
Data size not related to instance limit.
- ✗
Use a smaller instance type.
Why it's wrong here
Smaller instances don't increase limit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a resource limit error with a performance or storage issue, leading them to choose dataset reduction or storage changes instead of addressing the actual AWS service quota.
Detailed technical explanation
How to think about this question
AWS service quotas are per-region, per-account limits on resources such as the number of ml.p3.2xlarge instances for SageMaker training. The 'ResourceLimitExceeded' error is thrown by the SageMaker API when the requested instance count exceeds the current quota. Spot instances (Option B) can help by using spare AWS capacity, which may bypass some quota restrictions, but they are not a guaranteed fix for all quota types and can be interrupted.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — 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 SageMaker training instances. — Option A is correct because the 'ResourceLimitExceeded' error indicates that the AWS account has reached its service quota for SageMaker training instances. Requesting a service quota increase through the AWS Service Quotas console or API allows the account to launch additional instances of the required type, directly resolving the limit issue.
What should I do if I get this MLS-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.
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Last reviewed: Jul 4, 2026
This MLS-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 MLS-C01 exam.
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