What should you use?

Your company is building custom models that integrate into microservices architecture on Azure Kubernetes Services (AKS).

The model is built by using Python and published to AKS.

You need to update the model and enable Azure Application Insights for the model.

What should you use?
A . the Azure CLI
B . MLNET Model Builder
C . the Azure Machine Learning SDK
D . the Azure portal

Answer: C

Explanation:

You can set up Azure Application Insights for Azure Machine Learning.

Application Insights gives you the opportunity to monitor:

– Request rates, response times, and failure rates.

– Dependency rates, response times, and failure rates.

– Exceptions.

Requirements include an Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed.

References: https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/service/how-to-enable-app-insights