Configure Azure Databricks Cluster for Machine Learning Experiments | DP-100 Exam Preparation

Azure Databricks Cluster Configuration for Machine Learning Experiments

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Question

For your machine learning experiments, you want to use an Azure Databricks cluster as a training compute.

You already have your ML environment set up and now you want to configure the Databricks cluster for use.

You enter the Azure ML Studio, select the Training clusters and start creating the Databricks cluster.

Is that the right way to reach your goal?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B.

Correct Answer: B.

Option A is incorrect because you cannot create unmanaged compute targets (like Databricks clusters) from Azure ML directly.

Option B is CORRECT because you cannot create a Databricks cluster from the Azure ML environment.

The Azure Databricks workspace must be created first (from Azure Portal, for example), after setting up the DB cluster, it must be then attached to your Azure ML workspace.

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References:

No, that is not the right way to configure an Azure Databricks cluster for use in machine learning experiments.

Azure Databricks is a separate service from Azure Machine Learning, even though both can be used for machine learning. Azure Machine Learning is a platform for building, training, and deploying machine learning models, while Azure Databricks is a collaborative, cloud-based platform for data engineering, data science, and machine learning.

To configure an Azure Databricks cluster for use in machine learning experiments, you should follow these steps:

  1. Create an Azure Databricks workspace: You can create an Azure Databricks workspace from the Azure portal or the Azure Databricks portal.
  2. Create a cluster: In the Azure Databricks workspace, create a new cluster or use an existing one. You can configure the cluster size, autoscaling, and other options based on your requirements.
  3. Configure Azure Machine Learning: In the Azure Machine Learning workspace, create or use an existing compute target of type Azure Databricks. You can specify the Azure Databricks workspace and cluster details, and also configure other options such as the Python environment and the maximum number of nodes.
  4. Use the Azure Databricks cluster for machine learning: In your machine learning code, specify the Azure Databricks compute target and run your experiments. The code will be executed on the specified Azure Databricks cluster, and the results will be recorded in the Azure Machine Learning workspace.

Therefore, the correct way to configure an Azure Databricks cluster for use in machine learning experiments is to create an Azure Databricks workspace, create a cluster, configure Azure Machine Learning to use the cluster, and then run your experiments using the specified compute target.

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