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?

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.








 

Suggested Answer: C



This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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