AI-300: Operationalizing Machine Learning and Generative AI SolutionsDemo

By Microsoft · Question Mode

QuestionQ12

Implement machine learning model lifecycle and operations

A data science team trains a classification model to predict loan-approval outcomes.

Before registering the model, the team must ensure the following:

  • Predictions do not disproportionately affect protected groups.
  • Prediction errors can be assessed across different data segments.

You need to determine whether the model fulfills 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
  • A Analyze error rates across the global cohort.
  • B Measure endpoint latency under load.
  • C Validate inference schema compatibility.
  • D Evaluate feature importance for prediction transparency.
  • E Analyze error rates across defined demographic cohorts.
Explanation

Fairness assessment compares prediction outcomes and performance metrics, including error rates, across cohorts defined by sensitive demographic attributes to identify disparate impact. Feature-importance analysis supports the Responsible AI transparency principle by explaining which features influence predictions. Aggregate error analysis alone can conceal subgroup disparities.

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