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.
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You train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?

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 dat a. 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.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric. Solution: Run the following code:

Does the solution meet the goal?

You deploy a model in Azure Container Instance.
You must use the Azure Machine Learning SDK to call the model API.
You need to invoke the deployed model using native SDK classes and methods.
How should you complete the command? To answer, select the appropriate options in the answer areas.
NOTE: Each correct selection is worth one point.

You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You have machine learning models produce unfair predictions across sensitive features.
You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness.
You need to select a post-processing technique and model type.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.