You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no change to the deployed endpoint configuration.
You need to provide application developers with the information they need to consume the endpoint.
Which two values should you provide to application developers? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A . The name of the AKS cluster where the endpoint is hosted.
B . The name of the inference pipeline for the endpoint.
C . The URL of the endpoint.
D . The run ID of the inference pipeline experiment for the endpoint.
E . The key for the endpoint.
Answer: CE
Explanation:
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.
You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
Example:
# URL for the web service
scoring_uri = ‘<your web service URI>’
# If the service is authenticated, set the key or token key = ‘<your key or token>’
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service
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