Designing and Implementing a Data Science Solution on Azure — Question 81

You create an Azure Machine Learning workspace. You are implementing hyperparameter tuning for a model training from a notebook.

You must configure a Bandit termination policy that provides the following outcome:

If the value of the primary metric of AUC is 0.8 at the point of evaluation intervals, any run with the primary metric value below 0.66 will be terminated.

You need to identify which Bandit termination policy configuration to use.

What should you identify?

Answer options

• A. Set slack_amount to 0.2.
• B. Set slack_factor to 0.1.
• C. Set slack_factor to 0.2.
• D. Set slack_amount to 0.1.

Correct answer: C

Explanation

The correct answer is C, setting slack_factor to 0.2, which allows for a threshold that ensures any run with a primary metric below 0.66 will be terminated when AUC is at 0.8. The other options either set the slack_amount or slack_factor incorrectly, which would not meet the requirements for terminating the runs based on the specified AUC metric.