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AIF-C01 Practice Question: A data scientist is using Amazon SageMaker to…
A data scientist is using Amazon SageMaker to train a deep learning model. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause of this error?
⚠ Common exam trap
AWS often tests the distinction between resource limits (service quotas) and runtime errors (data corruption, memory, syntax) to see if candidates understand that 'ResourceLimitExceeded' is an AWS infrastructure constraint, not a model or code 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 concurrent training jobs or instance usage
The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has exceeded the service quota for concurrent training jobs or the total number of instances being used. This is a common limit enforced by AWS to prevent resource overconsumption, and it can be resolved by requesting a quota increase via the Service Quotas console or by reducing the number of parallel jobs.
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 concurrent training jobs or instance usage
Why this is correct
The ResourceLimitExceeded error is thrown by SageMaker when the account's service quota for concurrent training jobs or ml instance usage is exhausted. Since the stem describes a training job failing immediately, the constraint satisfied is the per-account quota ceiling, not data, code, or IAM permissions.
- ✗
The training data contains corrupted files
Why it's wrong here
Corrupted training data causes read or deserialisation failures inside the training container, producing errors such as ClientError or a Python exception, not ResourceLimitExceeded, which SageMaker raises when a service quota or resource ceiling is breached. Data validation is the right control when ingestion quality, not capacity, is the concern.
- ✗
The model is too large for the chosen instance type
Why it's wrong here
SageMaker returns ResourceLimitExceeded when the account's service quota for that instance type is exhausted, not when the model outgrows the instance; an oversized model typically surfaces as an out-of-memory or CUDA error. Choosing a larger instance is the remedy for capacity-driven OOM failures, which is why this option tempts.
- ✗
The training script has a syntax error
Why it's wrong here
A syntax error in the training script fails at container start-up with a Python traceback reported in the CloudWatch logs, whereas ResourceLimitExceeded is emitted by the SageMaker control plane when a quota or resource ceiling is hit. Script debugging is the correct step when the job launches but the code itself is faulty.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-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 AIF-C01 exam.