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Answer: From DB-workspace, configure the Link Azure ML workspace option.
The correct approach is to link the Azure Databricks workspace to the Azure Machine Learning workspace using the 'Link Azure ML workspace' option from the Databricks workspace. This configuration allows MLflow metrics and artifacts from experiments run on DB-cluster to be automatically tracked in ML-workspace without requiring custom code, as the integration handles the tracking seamlessly. Option A (configuring Advanced Logging on the cluster) is incorrect as it doesn't establish the necessary workspace linkage. Options C and D (creating compute resources in ML-workspace) are unrelated to tracking experiments from Databricks and would require custom code to redirect tracking, which contradicts the requirement to minimize custom code.
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You have an Azure Machine Learning workspace named ML-workspace and an Azure Databricks workspace named DB-workspace, which contains a cluster named DB-cluster. You need to configure the environment so that MLflow metrics and artifacts from experiments run on DB-cluster are tracked to ML-workspace, while minimizing the amount of custom code required.
What should you you do?
A
From DB-cluster, configure the Advanced Logging option.
B
From DB-workspace, configure the Link Azure ML workspace option.
C
From ML-workspace, create an attached compute.
D
From ML-workspace, create a compute cluster.
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