Designing and Implementing a Data Science Solution on Azure — Question 125

You create an Azure Machine Learning workspace named workspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.

You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.

You need to continue to log metrics and artifacts from your data science code.

What should you do?

Answer options

• A. Instantiate the job class.
• B. Instantiate the MLCIient class.
• C. Log in to workspace1.
• D. Configure the tracking URL.

Correct answer: D

Explanation

The correct answer is D because configuring the tracking URL allows the notebook to correctly log metrics and artifacts to the appropriate location within Azure Machine Learning. The other options are not sufficient on their own to ensure that metrics and artifacts continue to be logged correctly in the cloud environment.