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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
You finish training the model and are ready to publish a predictive web service that will provide the users with the ability to specify the data source and the save location of the results. The model includes a Split Data module.
Which two actions should you perform to convert the Machine Learning experiment to a predictive web service? To answer, drag the appropriate actions to the correct targets. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/convert-training-experiment-to-scoring-experim

NEW QUESTION: 2

A. Option D
B. Option A
C. Option B
D. Option C
Answer: C,D

NEW QUESTION: 3
A customer would like to view the pool usage for a Copy-on-Write SnapShot (CoW) environment using
CCI. Which CCI command should the customer use?
A. raidvchkscan -v pid
B. inqraid -p pid
C. raidscan -p pid
D. pairdisplay -p pid
Answer: A

NEW QUESTION: 4

A. Option D
B. Option A
C. Option B
D. Option C
E. Option E
Answer: B
Explanation: