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NEW QUESTION: 1
When a user tries to delete an application, there is no trash bin icon. What type of application is this?
A. Third-Party BlackBerry Application
B. Core BlackBerry 7 (BB7) application
C. Enterprise Java application
D. Core BlackBerry 10 (BB10) application
Answer: D

NEW QUESTION: 2
ネットワークエンジニアは、フレームが不要なトランクポートでのフレームの非効率的なフラッディングを停止することにより、スイッチの帯域幅とリソース使用率を改善する必要があります。このタスクを達成するためにどのCisco IOS機能を使用できますか？
A. VLANアクセスマップ
B. スイッチポートトランク許可VLAN
C. アクセスリスト
D. VTPプルーニング
Answer: D

NEW QUESTION: 3
Your company has a hybrid deployment of Office 365 that uses single sign-on. The company uses the contoso.com SMTP domain. You have a server named ADFS_Server5 that has the Active Directory Federation Services (AD FS) 2.0 server role installed. A power failure occurs at the data center. Power is restored at the data center. You need to verify that AD FS functions on ADFS_Server5.
What should you do?
A. Run the Test-MapiConnectivity cmdlet.
B. Run the Test-Connectivity cmdlet.
C. Connect to https://ADFS_Server5.tailspintoys.com/adfs/ls/default.aspx
D. Connect to https://ADFS_Server5.contoso.com/adfs/fs/federationserverservice.asmx.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
To verify that a federation server is operational
References: https://technet.microsoft.com/en-us/library/cc780059(v=ws.10)

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. Yes
B. No
Answer: A
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