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Question 28 - DP-100 discussion

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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-cluster to run experiments from notebooks that you import into DB-workspace.

You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.

What should you do?

A.
From DB-cluster, configure the Advanced Logging option.
Answers
A.
From DB-cluster, configure the Advanced Logging option.
B.
From DB-workspace, configure the Link Azure ML workspace option.
Answers
B.
From DB-workspace, configure the Link Azure ML workspace option.
C.
From ML-workspace, create an attached compute.
Answers
C.
From ML-workspace, create an attached compute.
D.
From ML-workspace, create a compute cluster.
Answers
D.
From ML-workspace, create a compute cluster.
Suggested answer: B

Explanation:

Connect your Azure Databricks and Azure Machine Learning workspaces:

Linking your ADB workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace.

To link your ADB workspace to a new or existing Azure Machine Learning workspace

1. Sign in to Azure portal.

2. Navigate to your ADB workspace's Overview page.

3. Select the Link Azure Machine Learning workspace button on the bottom right.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks

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asked 02/10/2024
Kefash White
40 questions
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