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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 Execute Python Script module.
Does the solution meet the goal?

You manage an Azure Machine Learning workspace. The development environment tor 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 10 use a user-assigned identity.
Solution: Configure the IdentityConfiguration class with the appropriate identity type.
Does the solution meet the goal?

You use differential privacy to ensure your reports are private. The calculated value of the epsilon for your data is 1.8. You need to modify your data to ensure your reports are private. Which epsilon value should you accept for your data?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
Solution: Run the following code:

Does the solution meet the goal?

You have a dataset that is stored m an Azure Machine Learning workspace.
You must perform a data analysis for differentiate privacy by using the SmartNoise SDK.
You need to measure the distribution of reports for repeated queries to ensure that they are balanced Which type of test should you perform?