What are two possible ways to achieve this goal?

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

The experiment fails.

You need to troubleshoot the failed experiment.

What are two possible ways to achieve this goal? Each correct answer presents 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.

View Answer

Answer: BD

Explanation:

Use get_details_with_logs() to fetch the run details and logs created by the run.

You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.

Incorrect Answers:

A: You can view the metrics of a trained model using run.get_metrics().

E: get_output() gets the output of the step as PipelineData.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs