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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.
Start of repeated scenario
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.
End of repeated scenario.
The users will use different data sources that follow a standard format. The users will receive results in a standard format by using the fraud prediction web service. The results will be saved to a location specified by the users.
You need to provide the users with the ability to get results for different risk tolerances without affecting the calculation of the model.
Which three modules should be configured to use the Web Service Parameters? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Select Columns in Dataset
B. Evaluate Model
C. Time Series Anomaly Detection
D. Import Data
E. Export Data
Answer: B,D,E
Explanation:
Explanation/Reference:
Explanation:
A: From scenario: 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.
BD: An Azure Machine Learning web service is created by publishing an experiment that contains modules with configurable parameters. In some cases, you may want to change the module behavior while the web service is running. Web Service Parameters allow you to do this task.
A common example is setting up the Import Data module so that the user of the published web service can specify a different data source when the web service is accessed. Or configuring the Export Data module so that a different destination can be specified References: https://docs.microsoft.com/en-us/azure/machine-learning/studio/web-service-parameters

NEW QUESTION: 2
In order for an organization to have a successful cloud implementation,which of the following must be true?
A. The organization must have an in-house IT department.
B. The organization must perform and successfully complete a cloud readiness assessment.
C. The organization must have an unlimited budget for allocation of cloud resources.
D. The organization must have used virtualization technology in the past.
Answer: B

NEW QUESTION: 3
Which four modes are available on the Cisco TelePresence MCU MSE 8510 to connect to a gatekeeper? (Choose four.)
A. partial proxy mode.
B. H.225-routed mode.
C. Direct mode.
D. H.245-routed mode.
E. H.265-routed mode.
F. full proxy mode.
Answer: B,C,D,F

NEW QUESTION: 4
One limitation of descriptive analysis is that it:
A. violates the individual's right to privacy.
B. does not permit the precise determination of functional relationships.
C. does not allow the behavior analyst the opportunity to measure, record, and interpret the data on the
target behavior.
D. provides a less than adequate description of the topography and intensity of behavior.
Answer: B