Exam DP-100 topic 3 question 143 discussion

Actual exam question from Microsoft's DP-100

Question #: 143
Topic #: 3

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.

Show Suggested Answer

Suggested Answer: C 🗳️

by paperflying at April 27, 2023, 5:37 p.m.

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kay1101

7 months, 2 weeks ago

Selected Answer: C

quote from MS Doc: For example, consider a Bandit policy applied at interval 10. Assume that the best performing job at interval 10 reported a primary metric is 0.8 with a goal to maximize the primary metric. If the policy specifies a slack_factor of 0.2, any training jobs whose best metric at interval 10 is less than 0.66 (0.8/(1+slack_factor)) will be terminated. link:https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2#bandit-policy

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deyoz

11 months ago

its correct

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Tin_Tin

11 months, 3 weeks ago

correct. For example, consider a Bandit policy applied at interval 10. Assume that the best performing job at interval 10 reported a primary metric is 0.8 with a goal to maximize the primary metric. If the policy specifies a slack_factor of 0.2, any training jobs whose best metric at interval 10 is less than 0.66 (0.8/(1+slack_factor)) will be terminated.

upvoted 3 times

vprowerty

11 months, 3 weeks ago

Hi Tin_Tin are you preparing the exam now? Do you think the questions on this website are accurated?

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snegnik

1 year, 7 months ago

0.8/1+0.2 = 0.6(6) Correct

upvoted 4 times

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paperflying

1 year, 8 months ago

correct. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2#bandit-policy

upvoted 1 times

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