easyMultiple Choice
MLA-C01 A company uses SageMaker to train a model Practice Question
A company uses SageMaker to train a model. The training job is failing with an error "ResourceLimitExceeded". What is the most likely cause?
⚠ Common exam trap
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.
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.
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 is thrown when the account's service quota for concurrent or requested training instances is exceeded. SageMaker caps instance counts per instance type per region, so requesting more ml instances than the quota allows fails immediately, regardless of data or script correctness.
- ✗
The model artifact is too large to upload
Why it's wrong here
Artifact upload failures occur after training completes, producing S3 or permission errors, whereas ResourceLimitExceeded is raised at job launch when the requested instance quota cannot be satisfied. Larger artifact handling would instead be addressed through S3 multipart upload configuration or output compression.
- ✗
Invalid hyperparameters
Why it's wrong here
Malformed hyperparameters trigger a validation or ClientError at job creation, not ResourceLimitExceeded, which reflects insufficient instance quota or unavailable capacity. Tuning hyperparameter values is the correct action when a job starts but converges poorly or terminates with algorithm-specific errors.
- ✗
The training data size exceeds the available instance storage
Why it's wrong here
Instance storage exhaustion surfaces as disk-full or capacity errors during data download, not ResourceLimitExceeded, which SageMaker raises when the requested instance type or count cannot be provisioned in the account or Region. Choosing larger storage volumes would be the fix for genuinely oversized training datasets.
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