Explanation:
The question asks for two complete solutions to troubleshoot a failed Azure ML experiment run from a notebook. Option B (get_details_with_logs()) is correct because it retrieves both run details and execution logs directly through the SDK, which is optimal for programmatic troubleshooting in a notebook environment. Option D is correct because Azure ML Studio provides a comprehensive UI for viewing run logs, metrics, and diagnostics, offering an alternative visual approach. Option A (get_metrics()) is incorrect as it only retrieves metrics, not detailed execution logs needed for troubleshooting failures. Option C is incorrect because log files are not stored in the experiment folder but in dedicated log directories (e.g., azureml-logs), making direct file access unreliable. Option E (get_output()) is incorrect as it retrieves pipeline outputs, not logs.
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You are using the Azure Machine Learning SDK in a notebook to run an experiment that references a script file in an experiment folder. The experiment fails. What are two different ways to troubleshoot the failed run? Each answer must present a complete solution.
A
Use the get_metrics() method of the run object to retrieve the experiment run logs.
B
Use the get_details_with_logs() method of the run object to display the experiment run logs.
C
View the log files for the experiment run in the experiment folder.
D
View the logs for the experiment run in Azure Machine Learning studio.
E
Use the get_output() method of the run object to retrieve the experiment run logs.
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