Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure Exam Practice Test

You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.

A Synapse Spark Compute is currently attached and uses system-assigned identity.

You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity.

Solution: Initialize the DefaultAzureCredential class.

Does the solution meet the goal?

You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.

A Synapse Spark Compute is currently attached and uses system-assigned identity.

You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity.

Solution: Pass the UserAssignedldentity class object to the SynapseSparkCompute class.

Does the solution meet the goat?

You manage an Azure Machine Learning workspace named projl

You plan to use assets defined in projl to create a pipeline in the Machine Learning studio designer

You need to set the Registry name filter to display only the list of assets defined in projl.

What should you set the Registry name filter to?

You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count

You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?

You manage an Azure Machine Learning workspace named Workspace1.

You plan to create a pipeline in the Azure Machine Learning Studio designer. The pipeline must include a custom component You need to ensure the custom component can be used in the pipeline. What should you do first.

You use Azure Machine Learning Designer lo load the following datasets into an experiment:

Dataset1:

Dataset2:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Add Rows component.

Does the solution meet the goal?

You use Azure Machine Learning Designer to load the following datasets into an experiment:

Dataset1

Dataset2

You use Azure Machine Learning Designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Join Data component.

Does the solution meet the goal?