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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
Assuming that "client1" and "client2" are directly-connected networks, what is the result of executing the following BGP export policy?

A. Only entry 10 is executed for "client1" and "client2".
B. Entries 10 and 20 are executed for "client1", and entries 10, 20 and 30 are executed for
"client2".
C. Entry 10 is executed for directly-connected routes, entry 20 for "client1" and entry 30 for "client2".
D. Entries 10 and 20 are executed for "client1", and entries 10 and 30 are executed for "client2".
Answer: A

NEW QUESTION: 3

A. Option C
B. Option B
C. Option D
D. Option A
Answer: A
Explanation:
Explanation
The HttpRequest.Unvalidated property gets the HTTP request values without triggering request validation.
Request validation checks for HTML markup and script that might indicate a potential cross-site scripting attack. By default, all values are checked using request validation and if any values contain markup or script, ASP.NET throws an HttpRequestValidationException exception. Use this method if you anticipate that the request will contain markup (for example, you are allowing users to post content that contains markup) and you want to get the raw value of a request.
References: https://msdn.microsoft.com/en-us/library/system.web.httprequest.unvalidated.aspx