Quick Answer
The correct action is to request a limit increase for the instance type, because the ResourceLimitExceeded error in SageMaker signals that your AWS account has hit a service quota for that specific instance family, such as ml.p3.2xlarge, rather than a failure within your custom Docker container or training code. This error arises from a hard account-level cap on concurrent instances or total vCPUs, so the fix lies in the AWS Service Quotas console or a support ticket, not in modifying your container or reducing data size. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between infrastructure quota limits and application-level errors—a common trap is confusing ResourceLimitExceeded with InsufficientInstanceCapacity or OutOfMemory errors. Remember the mnemonic: "Quota, not quality"—the error is about how many instances you can run, not how well they perform.
This MLS-C01 practice question tests your understanding of modeling. 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 a custom Docker container in SageMaker for training. The training job fails with 'ResourceLimitExceeded' error. Which action should the data scientist take?
Use a smaller instance type
Why wrong: May still hit limit.
Reduce the number of epochs
Why wrong: May not resolve limit issue.
Request a limit increase for the instance type
Directly addresses the error.
Use a pre-built SageMaker container instead
Why wrong: Unrelated to resource limits.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Request a limit increase for the instance type
The 'ResourceLimitExceeded' error in SageMaker indicates that the AWS account has reached a service quota for the specified instance type (e.g., ml.p3.2xlarge). This is a quota limit, not a performance or resource exhaustion issue within the training job itself. The correct action is to request a limit increase via the AWS Service Quotas console or AWS Support, which raises the maximum number of concurrent instances or total vCPUs allowed for that instance family.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a smaller instance type
Why it's wrong here
May still hit limit.
Reduce the number of epochs
Why it's wrong here
May not resolve limit issue.
Request a limit increase for the instance type
Why this is correct
Directly addresses the error.
Related concept
Read the scenario before looking for a memorised answer.
Use a pre-built SageMaker container instead
Why it's wrong here
Unrelated to resource limits.
Common exam traps
AWS often tests the misconception that 'ResourceLimitExceeded' is a performance or memory error, leading candidates to choose instance downsizing or epoch reduction, when in fact it is a strict AWS account quota that must be raised through a formal request.
Detailed technical explanation
SageMaker training jobs use AWS service quotas (formerly called limits) that are per-region and per-instance-family, such as a maximum of 5 concurrent ml.p3.2xlarge instances. The 'ResourceLimitExceeded' error is thrown by the SageMaker API when the requested instance count would exceed this quota, even if the account has sufficient EC2 On-Demand capacity. To diagnose, you can use the AWS CLI command 'aws service-quotas get-service-quota --service-code sagemaker --quota-code L-3819A6DF' (for SageMaker training job instance count) to check the current limit and then submit a quota increase request.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Request a limit increase for the instance type — The 'ResourceLimitExceeded' error in SageMaker indicates that the AWS account has reached a service quota for the specified instance type (e.g., ml.p3.2xlarge). This is a quota limit, not a performance or resource exhaustion issue within the training job itself. The correct action is to request a limit increase via the AWS Service Quotas console or AWS Support, which raises the maximum number of concurrent instances or total vCPUs allowed for that instance family.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
Keep practising
Last reviewed: Jun 30, 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.
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.