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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. Import Data
B. Export Data
C. Evaluate Model
D. Time Series Anomaly Detection
E. Select Columns in Dataset
Answer: A,B,C
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
Some customers have reported receiving an untrusted certificate warning when visiting the company's website. The administrator ensures that the certificate is not expired and that customers have trusted the original issuer of the certificate. Which of the following could be causing the problem?
A. The intermediate CA certificates were not installed on the server.
B. The certificate is not the correct type for a virtual server.
C. The client's browser is trying to negotiate SSL instead of TLS.
D. The encryption key used in the certificate is too short.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
In a hierarchical trust model, also known as a tree, a root CA at the top provides all of the information. The intermediate CAs are next in the hierarchy, and they trust only information provided by the root CA. The root CA also trusts intermediate CAs that are in their level in the hierarchy and none that aren't.
NEW QUESTION: 3
Which tools can be used to reduce the number of existing duplicate records?
A. Dashboard Pack
B. Apex Trigger
C. Merge Wizard
D. App Exchange solutions
Answer: C,D
NEW QUESTION: 4
You have an Azure virtual machine named VM1 that connects to a virtual network named VNet1. VM1 has the following configurations:
* Subnet: 10.0.0.0/24
* Availability set: AVSet
* Network security group (NSG): None
* Private IP address: 10.0.0.4 (dynamic)
* Public IP address: 40.90.219.6 (dynamic)
You deploy a standard, Internet-facing load balancer named slb1.
You need to configure slb1 to allow connectivity to VM1.
Which changes should you apply to VM1 as you configure slb1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
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