What should you do?

You deploy a real-time inference service for a trained model.

The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

You need to implement a monitoring solution for the deployed model using minimal administrative effort.

What should you do?
A . View the explanations for the registered model in Azure ML studio.
B . Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
C . View the log files generated by the experiment used to train the model.
D . Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

Answer: B

Explanation:

Configure logging with Azure Machine Learning studio

You can also enable Azure Application Insights from Azure Machine Learning studio. When you’re ready to deploy your model as a web service, use the following steps to enable Application Insights: