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Machine Learning Implementation and OperationseasyMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

Network Topology
$ aws sagemaker list-training-jobsmax-results 10Refer to the exhibit."TrainingJobSummaries": ["TrainingJobName": "job-1","TrainingJobStatus": "Completed","CreationTime": "2023-06-01T10:00:00Z","TrainingEndTime": "2023-06-01T11:00:00Z"},"TrainingJobName": "job-2","TrainingJobStatus": "Failed","CreationTime": "2023-06-01T12:00:00Z","TrainingEndTime": "2023-06-01T12:30:00Z"

A data scientist runs the AWS CLI command shown in the exhibit. The output shows that job-2 failed. Which action should the data scientist take to diagnose the failure?

⚠ Common exam trap

Test-takers frequently assume CloudWatch Logs are always available for failed jobs, but SageMaker only writes to CloudWatch after the training container starts, so a pre-start failure (e.g., insufficient instance capacity) will have no logs, making `describe-training-job` the correct first diagnostic step.

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

Run `aws sagemaker describe-training-job --training-job-name job-2` to see the failure reason

The `describe-training-job` API call returns a `FailureReason` field that provides the specific error message for a failed SageMaker training job. This is the most direct and efficient way to diagnose why job-2 failed, as it retrieves the exact failure reason from the SageMaker service without requiring additional log parsing or bucket inspection.

Answer analysis

Option-by-option breakdown

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

  • Check the CloudWatch Logs log group for job-2

    Why it's wrong here

    CloudWatch logs are useful but the first step is to get the failure reason from the API.

  • Check the S3 bucket for any error logs uploaded by the training job

    Why it's wrong here

    SageMaker does not automatically upload error logs to S3.

  • Run `aws sagemaker list-training-jobs --name-contains job-2` to get more details

    Why it's wrong here

    ListTrainingJobs does not provide failure details.

  • Run `aws sagemaker describe-training-job --training-job-name job-2` to see the failure reason

    Why this is correct

    DescribeTrainingJob includes a FailureReason field.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This MLS-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 MLS-C01 exam.