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MLA-C01 Practice Question: During model training on Amazon SageMaker, the…
During model training on Amazon SageMaker, the training job fails with a 'ResourceLimitExceeded' error. What is the most likely cause?
⚠ Common exam trap
AWS often tests the distinction between resource-level errors (quotas) versus data-level or code-level errors; the trap here is confusing a 'ResourceLimitExceeded' error with a dataset size issue or a training script bug, leading candidates to pick Option B or C.
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's instance limit for the chosen instance type has been reached
The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has reached its service quota for the specified instance type. Each AWS account has default limits on the number of concurrent instances (e.g., ml.p3.2xlarge) that can be used for training jobs. When a training job requests more instances than the account's limit allows, SageMaker throws this error. This is distinct from dataset size or algorithmic issues.
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 algorithm's learning rate is too high
Why it's wrong here
An excessive learning rate causes divergence or NaN loss during training, appearing as poor metrics rather than a provisioning error. It is tempting because hyperparameter faults do ruin jobs, but ResourceLimitExceeded is returned at resource-allocation time, when the requested instance type or count exceeds the SageMaker quota.
- ✗
The dataset is too large for the instance
Why it's wrong here
A large dataset exhausts disk or memory and surfaces as capacity errors, but ResourceLimitExceeded specifically reports that the requested instance type or count exceeds your SageMaker service quota. It is tempting because oversized data genuinely strains instances, yet quota exhaustion is the actual trigger.
- ✗
The training script has a syntax error
Why it's wrong here
A syntax error aborts the script during execution and surfaces as a training failure with a Python traceback, not a quota rejection. It is tempting because script faults do fail jobs, but ResourceLimitExceeded is raised before the container starts, when the requested instance count breaches the account quota.
- ✓
The account's instance limit for the chosen instance type has been reached
Why this is correct
SageMaker training jobs consume ML compute instances from your account's regional quota. When the requested instance type's concurrent limit is already fully consumed, the job cannot provision capacity and fails immediately with ResourceLimitExceeded, rather than a data, IAM, or algorithm error.
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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.