You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster. You must use DB-c

Question

Exhibit

Options

• AFrom DB-cluster, configure the Advanced Logging option.
• BFrom DB-workspace, configure the Link Azure ML workspace option.
• CFrom ML-workspace, create an attached compute.
• DFrom ML-workspace, create a compute cluster.

• A
  2% (1)
• B
  93% (56)
• C
  2% (1)
• D
  3% (2)

Explanation

Linking the Azure Databricks workspace to the Azure ML workspace (using the 'Link Azure ML workspace' option from DB-workspace settings) automatically configures the Databricks MLflow tracking URI to point to the Azure ML workspace's tracking server. Once linked, any MLflow calls in notebooks running on DB-cluster are captured in ML-workspace with no custom tracking code required. Option A (Advanced Logging) enables Databricks-internal logging, not Azure ML integration. Option C (creating attached compute in ML-workspace) registers a Databricks cluster as compute for Azure ML pipelines-different from MLflow experiment tracking. Option D (compute cluster) is for Azure ML managed training, unrelated to Databricks MLflow tracking.

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