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NEW QUESTION: 1
Which of the following best describes the identification, analysis, and ranking of risks?
A. Fast tracking
B. Fixed-price contracts
C. Plan Risk management
D. Design of experiments
Answer: C

NEW QUESTION: 2
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 have a Microsoft Azure Active Directory (Azure AD) tenant named contoso.com.
You create an Azure Advanced Threat Protection (ATP) workspace named Workspace1.
The tenant contains the users shown in the following table.

You need to modify the configuration of the Azure ATP sensors.
Solution: You instruct User2 to modify the Azure ATP sensor configuration.
Does this meet the goal?
A. Yes
B. No
Answer: B

NEW QUESTION: 3
Which 802.1x component enforces the network access policy?
A. authentication server
B. RADIUS server
C. authenticator
D. supplicant
Answer: A

NEW QUESTION: 4
You are building a classification experiment in Azure Machine Learning.
You need to ensure that you can use the Evaluate Model module in the experiment.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Connect the output of the Score Model modules to the input of the Evaluate Model module.
B. Connect the output of the Score Model modules to the input of the Train Model modules and the input of the Split Data modules.
C. Connect the input of the Score Model modules to the output of the Train Model modules and the output Split Data modules.
D. Connect the input of the Score Model modules to the output of the Evaluate Model module.
Answer: A,C
Explanation:
Explanation/Reference:
Explanation:
Use the training data.
To evaluate a model, you must connect a dataset that contains a set of input columns and scores. If no other data is available, you can use your original dataset.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
Use testing data
A common scenario in machine learning is to separate your original data set into training and testing datasets, using the Split module, or the Partition and Sample module.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Connect the output of the Split Data module that contains the testing data to the right-hand input of Evaluate Model.
3. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model