Q: 29

DRAG DROP You have several machine learning models registered in an Azure Machine Learning workspace. You must use the Fairlearn dashboard to assess fairness in a selected model. 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.

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Select a binary classification or regression model.
Select a metric to be measured.
Select a multiclass classification model.
Select a model feature to be evaluated.
Select a clustering model.

Correct Answer:

Answer Areas Action 1: A. Select a binary classification or regression model. Action 2: D. Select a model feature to be evaluated. Action 3: B. Select a metric to be measured.

Explanation

To assess fairness using the Fairlearn dashboard in Azure Machine Learning, you must follow a specific configuration flow:

Model Selection: First, you must identify the supported model type. The Fairlearn dashboard primarily supports binary classification and regression models. (While the open-source package has expanded, the Azure integration and this specific exam question focus on these core supported types, excluding clustering).
Feature Selection: Next, you must select the sensitive feature (model feature to be evaluated), such as "Gender" or "Race." This allows the dashboard to slice the data into cohorts for comparison.
Metric Selection: Finally, you select the performance metric to be measured (e.g., Accuracy, Precision) and the specific fairness metric (e.g., Demographic Parity difference) to quantify the disparity between the selected groups.

References

Source 1: Microsoft Learn. "Assess model fairness in Azure Machine Learning." This documentation outlines the supported scenarios (classification and regression) and the workflow of configuring the dashboard by defining sensitive features and performance metrics.

Relevant Quote: "Fairlearn allows you to assess the fairness of your model... It supports classification and regression models." "To assess fairness, you need to... select the sensitive feature... select the performance metric."

URL: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

Source 2: Fairlearn Official Documentation (User Guide - Dashboard).

Relevant Context: The dashboard setup wizard explicitly asks users to select the sensitive feature first ("Select a sensitive feature") before asking for the performance metric ("Select a performance metric").

URL: https://fairlearn.org/v0.7.0/user_guide/dashboard.html (Note: Fairlearn is the underlying open-source tool integrated into Azure ML).

Source 3: Microsoft Learn - "Use the Fairlearn dashboard".

Step-by-step confirmation: The UI workflow guides the user to "Select the attribute" (Feature) -> "Select the metric" (Metric).

URL: https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

DP-100 Exam Dumps 2026 – Azure Data Scientist Associate Quiz

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