AIF-C01 Fundamentals of AI and ML Practice Question

Exhibit

Refer to the exhibit.

```json
{
  "TrainingJobStatus": "Failed",
  "FailureReason": "ClientError: ValidationException: The instance type ml.p3.2xlarge is not supported in the requested region (us-east-1)."
}
```

This is the output of `aws sagemaker describe-training-job --training-job-name my-training-job`.

Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker and it failed. Which action should the data scientist take FIRST to resolve the issue?

⚠ Common exam trap

AWS often tests the distinction between capacity unavailability (which requires switching instance types) and service limits (which require a limit increase), leading candidates to mistakenly request a limit increase when the real issue is temporary capacity constraints.

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

✓

Use a different instance type that is available in the region

The error indicates that the requested instance type is not available in the current region due to capacity constraints. The first step is to switch to a different instance type that is available in the same region, as this is the quickest and most direct way to resolve the provisioning failure without requiring service limit increases or changing regions.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Request a service limit increase for the instance type
Why it's wrong here
A limit increase would not make an unsupported instance type available.
✗
Use a different AWS region
Why it's wrong here
Changing the AWS region does not address the root cause of a failed SageMaker training job, which is typically a misconfigured resource, insufficient instance quota, or a code error in the training script. It is tempting because region selection can resolve latency or service-availability issues, and would be correct if the failure stemmed from a regional service outage or a lack of supported instance types in the current region.
✗
Enable spot training
Why it's wrong here
Spot instances do not bypass the instance type availability check.
✓
Use a different instance type that is available in the region
Why this is correct
The error clearly states the instance type is unsupported; switching to an available type resolves it.

Visual reference

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AIF-C01 Fundamentals of AI and ML Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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