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NEW QUESTION: 1
Sie haben einen Azure Active Directory-Mandanten mit dem Namen contoso.com, der die in der folgenden Tabelle aufgeführten Benutzer enthält.

Die Multi-Factor-Authentifizierung (MFA) ist für die Verwendung von 131.107.50 / 24 für vertrauenswürdige IPs konfiguriert.
Der Mandant enthält die in der folgenden Tabelle aufgeführten benannten Speicherorte.

Sie erstellen eine Richtlinie für den bedingten Zugriff mit den folgenden Konfigurationen:
* Benutzer- und Gruppenzuordnung: Alle Benutzer
* Zuweisung von Cloud-Apps: App1
* Bedingungen: Alle vertrauenswürdigen Speicherorte einschließen
* Zugriff gewähren: Multi-Faktor-Authentifizierung erforderlich
Wählen Sie für jede der folgenden Anweisungen Ja aus, wenn die Anweisung wahr ist. Andernfalls wählen Sie Nein.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.

Answer:
Explanation:

Explanation

Box 1: Yes
131.107.50.10 is in a Trusted Location so the conditional access policy applies. The policy requires MFA so User1 must use MFA.
Box 2: Yes.
131.107.50.15 is in a Trusted Location so the conditional access policy applies. The policy requires MFA so User2 must use MFA.
Box 3: No.
131.107.5.5 is not in a Trusted Location so the conditional access policy does not apply. Therefore, User2 does not need to use MFA.
Reference:
https://docs.microsoft.com/en-us/azure/active-directory/conditional-access/location-condition

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 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

NEW QUESTION: 3
The administration tool is the developer's interface to the repository file. It allows the development of and changes to the metadata repository file. Which statement is true?
A. All aspects of security are being held in the repository file.
B. Multiple developers can work on one repository at the same time. Results can be merged later on.
C. Changes to the repository can only be carried out in offline mode.
D. Opening the rpd.file in online mode results in performance Issues for the application user.
Answer: B
Explanation:
Explanation
The Oracle BI repository development process adheres to the classic Software Configuration Management (SCM)process, which utilizes a three-way merge to manage concurrent development.
The merge process involves three repositories:
* The (Original|Received|Master|Parent) repository. The repository that you received before changed. You must keep it preciously unchanged.
* The Modified repository. The repository that contains the customizations you made to the original repository.
* The Current repository. Generally, the repository that is in production and where some customisations could have been performed by other people.
In a multiuser environment (MUDE), Developers check out the file and make changes locally. Then, these changes are automatically reconciled and merged into the master repository.