Exam AI-300 Topic 1 Question 64 Discussion
Actual exam question for Microsoft's AI-300 exam
Question #: 64
Topic #: 1
Question #: 64
Topic #: 1
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Suggested Answer: D,E Vote an answer
A loan approval model must be assessed for both transparency and demographic fairness before registration.
Option D (feature importance) addresses prediction transparency - using SHAP or Azure ML ' s Responsible AI dashboard, the team can see which features drive individual predictions, revealing whether proxy variables for protected attributes are influencing decisions inappropriately. Option E (error rates across demographic cohorts) directly measures whether the model makes more mistakes for specific groups - a core fairness metric required under Responsible AI principles. Option A (global error rates) masks disparities since you need cohort-level analysis. Option B (endpoint latency) is a performance engineering concern. Option C (inference schema compatibility) is a technical integration check. The Azure ML Responsible AI dashboard combines both error analysis and feature importance in a unified interface designed for exactly this type of pre-deployment fairness assessment.
Microsoft Learn Reference Topic: Responsible AI dashboard in Azure Machine Learning - Error analysis and model interpretability
Option D (feature importance) addresses prediction transparency - using SHAP or Azure ML ' s Responsible AI dashboard, the team can see which features drive individual predictions, revealing whether proxy variables for protected attributes are influencing decisions inappropriately. Option E (error rates across demographic cohorts) directly measures whether the model makes more mistakes for specific groups - a core fairness metric required under Responsible AI principles. Option A (global error rates) masks disparities since you need cohort-level analysis. Option B (endpoint latency) is a performance engineering concern. Option C (inference schema compatibility) is a technical integration check. The Azure ML Responsible AI dashboard combines both error analysis and feature importance in a unified interface designed for exactly this type of pre-deployment fairness assessment.
Microsoft Learn Reference Topic: Responsible AI dashboard in Azure Machine Learning - Error analysis and model interpretability
by Nicholas at Jun 19, 2026, 03:49 AM
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