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NEW QUESTION: 1
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

NEW QUESTION: 2
Which command will only show the number of entries in the connection table?
A. fw tab -t connections -s
B. fw tab -t connections -u
C. fw tab
D. fw tab -t connections
Answer: A

NEW QUESTION: 3
Sie haben einen Server namens Server1, auf dem Windows Server 2016 ausgeführt wird. Server1 ist ein Hyper-V-Host.
Sie führen die in der folgenden Grafik gezeigten Befehle aus:

Verwenden Sie die Dropdown-Menüs, um die Antwortauswahl auszuwählen, die die einzelnen Anweisungen anhand der in der Grafik angezeigten Informationen vervollständigt.

Answer:
Explanation:

Explanation

https://technet.microsoft.com/en-us/itpro/powershell/windows/hyper-v/add-vmswitchteammember

NEW QUESTION: 4
DRAG DROP


Answer:
Explanation: