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Question 8 - DP-100 discussion

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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.
Answers
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.
Answers
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.
Answers
C.
View the log files generated by the experiment used to train the model.
D.
Answers
D.
Suggested answer: B

Explanation:

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:

1. Sign in to the studio at https://ml.azure.com.

2. Go to Models and select the model you want to deploy.

3. Select +Deploy.

4. Populate the Deploy model form.

5. Expand the Advanced menu.

6. Select Enable Application Insights diagnostics and data collection.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

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asked 02/10/2024
john ignacio echavarria lopez
33 questions
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