- A
The account has reached the limit for number of training instances
ResourceLimitExceeded occurs when you exceed a service quota, such as instance count.
- B
The model artifact is too large to upload
Why wrong: Upload size limits cause timeouts or size errors, not ResourceLimitExceeded.
- C
Invalid hyperparameters
Why wrong: Invalid hyperparameters cause a validation error, not ResourceLimitExceeded.
- D
The training data size exceeds the available instance storage
Why wrong: Storage issues typically cause disk space errors, not resource limits.
Question 855 of 1,000
easyMultiple ChoiceObjective-mapped
Resolving ResourceLimitExceeded Error in SageMaker Training
This MLA-C01 practice question tests your understanding of a company uses sagemaker to train a model. 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 uses SageMaker to train a model. The training job is failing with an error "ResourceLimitExceeded". What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The account has reached the limit for number of training instances
The 'ResourceLimitExceeded' error in SageMaker indicates that your AWS account has reached a service quota for a specific resource, such as the number of training instances. SageMaker enforces per-region limits on the number of ml.* instances that can be used concurrently for training jobs, and exceeding this quota triggers the error. This is a common issue when scaling up training without requesting a limit increase.
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.
- ✓
The account has reached the limit for number of training instances
Why this is correct
ResourceLimitExceeded occurs when you exceed a service quota, such as instance count.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model artifact is too large to upload
Why it's wrong here
Upload size limits cause timeouts or size errors, not ResourceLimitExceeded.
- ✗
Invalid hyperparameters
Why it's wrong here
Invalid hyperparameters cause a validation error, not ResourceLimitExceeded.
- ✗
The training data size exceeds the available instance storage
Why it's wrong here
Storage issues typically cause disk space errors, not resource limits.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The AWS ML Engineer Associate exam often tests the distinction between resource limits (quotas) and other failure modes like storage or validation errors, so candidates mistakenly choose options related to data size or hyperparameters when the error message explicitly points to a quota issue.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker training jobs allocate EC2 instances from your account's service quotas, which are enforced at the regional level via the AWS Service Quotas API. For example, the default limit for ml.m5.large training instances is often 20 per region, and exceeding this triggers a 'ResourceLimitExceeded' exception with HTTP status code 400. In real-world scenarios, teams using distributed training (e.g., with PyTorch DDP) can quickly hit this limit if they launch multiple jobs simultaneously without requesting a quota increase through the AWS Support Center.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The account has reached the limit for number of training instances — The 'ResourceLimitExceeded' error in SageMaker indicates that your AWS account has reached a service quota for a specific resource, such as the number of training instances. SageMaker enforces per-region limits on the number of ml.* instances that can be used concurrently for training jobs, and exceeding this quota triggers the error. This is a common issue when scaling up training without requesting a limit increase.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 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.
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