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NEW QUESTION: 1
You have an Azure Active Directory (Azure AD) tenant named Contoso.com that is synced to an Active Directory domain.
The tenant contains the users shown in the following table.

The user have the attributes shown in the following table.

You need to ensure that you can enable Azure Multi-Factor Authentication (MFA) for all four users.
Solution: You create a new user account in Azure AD for User3.
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
User3 requires a user account in Azure AD.
Note: Your Azure AD password is considered an authentication method. It is the one method that cannot be disabled.
References:
https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-authentication-methods

NEW QUESTION: 2
For immediate annuities, this is the , defined by the sequence of periodic annuity benefit payments the policyholder is promised.
A. implicit interest rate
B. minimum credited rate
C. maximum credited rate
D. explicit interest rate
Answer: A

NEW QUESTION: 3
A new security flaw and fix has been published about a laptop's OS. Which of the following can be implemented to prevent exploitation?
A. User training
B. Enable hard drive encryption
C. Patching policy
D. Enable screen lock
Answer: C
Explanation:
Reference:http://www.computerweekly.com/feature/Microsoft-patch-management-policy
Topic 4, Printers

NEW QUESTION: 4
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 an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to CSV module to the Azure ML experiment and then open the module output in a new notebook.
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Access intermediate datasets from Machine Learning experiments
The following steps show an example that creates an experiment, runs it and accesses the intermediate dataset.
1. Create a new experiment.
2. Insert an Adult Census Income Binary Classification dataset module.
3. Insert a Split module, and connect its input to the dataset module output.
4. Insert a Convert to CSV module and connect its input to one of the Split module outputs.
5. Save the experiment, run it, and wait for it to finish running.
6. Click the output node on the Convert to CSV module.
7. When the context menu appears, select Generate Data Access Code
8. Select the code snippet and copy it to your clipboard from the window that appears..
9. Paste the code in your notebook.
Note: After an experiment is run in the Machine Learning Studio, it is possible to access the intermediate datasets from the output nodes of modules. Intermediate datasets are data that has been created and used for intermediate steps when a model tool has been run.
Intermediate datasets can be accessed as long as the data format is compatible with the Python client library.
The following formats are supported (constants for these are in the azureml.DataTypeIds class):
PlainText

GenericCSV

GenericTSV

GenericCSVNoHeader

GenericTSVNoHeader

References: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python- data-access