Answer-first summary for fast verification
Answer: Configure the tracking URL.
The correct answer is D because when moving MLflow tracking from a local environment to an Azure Machine Learning compute instance within the same workspace, you need to configure the tracking URL to point to the Azure ML workspace. Each Azure ML workspace has its own tracking URI with the 'azureml://' protocol. This configuration allows MLflow to properly route metrics and artifacts to the workspace's tracking store. While logging into the workspace (option C) might be necessary for authentication, the specific MLflow configuration requirement is setting the tracking URL. Options A and B are incorrect as they don't address the MLflow tracking configuration needed for the transition from local to Azure ML compute.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
You have an Azure Machine Learning workspace named "workspace1" containing a Python SDK v2 notebook that uses MLflow to log model training metrics and artifacts from your local computer. You now need to run this same notebook on an Azure Machine Learning compute instance within workspace1 while maintaining the ability to log metrics and artifacts from your data science code.
What should you do?
A
Instantiate the job class.
B
Instantiate the MLCIient class.
C
Log in to workspace1.
D
Configure the tracking URL.
No comments yet.