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NEW QUESTION: 1
HOTSPOT
You are the Exchange Online administrator for an organization.
The organization migrates all users to Exchange Online.
An employee works for a partner organization named Contoso, Ltd. The employee uses the email alias employeel contoso.com.
Users report that over the past week, they have not received email messages from [email protected].
You need to trace email messages that originate from [email protected] to users inside your organization.
In the message trace window, which two settings should you configure? To answer, select the appropriate objects in the answer area.

Answer:
Explanation:

NEW QUESTION: 2
Match the service on the right that evaluates each exposure type on the left.
(Select your answer from the pull-down list. Answers may be used more than once or not at all.)

Answer:
Explanation:

Reference:
https://www.paloaltonetworks.com/prisma/cloud/cloud-data-security

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a PFIExplainer.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability