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NEW QUESTION: 1
Answer:
Explanation:
Explanation
VM1: A shielded virtual machine
VM2: An encryption-supported virtual machine
VM3: An encryption-supported virtual machine
Shielded VM Prevents Virtual Machine connection and PowerShell Direct, it prevent the Hyper-V host to interactin any means with the Shielded VM.
https://docs.microsoft.com/en-us/windows-server/virtualization/guarded-fabric-shielded-vm/guarded-fabric-ands
NEW QUESTION: 2
The Hall Health Plan gathered objective clinical information about the recommended uses and dosages of angiotensin-converting enzyme (ACE) inhibitors and presented the information to network providers to illustrate the appropriate use of these frequently prescribed and expensive drugs. This information indicates that Hall most likely educated its network providers through the use of
A. counter detailing
B. drug efficacy study implementation (DESI)
C. detailing
D. cognitive services
Answer: A
NEW QUESTION: 3
You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.
What should the entry script do?
A. Create a Conda environment for the web service compute and install the necessary Python packages.
B. Register the model with appropriate tags and properties.
C. Start a node on the inference cluster where the web service is deployed.
D. Load the model and use it to predict labels from input data.
E. Specify the number of cores and the amount of memory required for the inference compute.
Answer: D
Explanation:
Explanation
The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.
The two things you need to accomplish in your entry script are:
Loading your model (using a function called init())
Running your model on input data (using a function called run())
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where
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