Azure ML Responsible AI Dashboard: Counterfactuals and Causal Components
You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible AI dashboard. Based on what you learn from the dashboard, you must perform the following activities: • Determine what must be done to get a desirable outcome from the model. • Identify the features that have the most direct effect on your outcome of interest. You need to select the components to use for the Responsible AI dashboard configuration. Which two components should you add? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Community Votes
100% of anonymous learners picked answer BD. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam distinguishes between predictive feature importance (Explanation) and causal impact; you must select 'Counterfactuals' for actionable 'what-if' scenarios and 'Causal' for identifying direct feature effects.
This question tests the specific capabilities of the Azure Machine Learning Responsible AI dashboard components. The consensus is that 'Counterfactuals' help determine actions for desirable outcomes, while 'Causal' inference identifies features with direct effects on the outcome.
Candidates often choose 'Explanation' instead of 'Causal'. While Explanation shows feature importance, it does not necessarily establish causality or direct effect as required by the second part of the scenario.
Community Discussion (4 comments)
Comments & Corrections
No comments yet — spotted an error or have a note? Share it below.
Expert Analysis
Why the Answer Is Correct
The Azure ML Responsible AI dashboard offers specific components for different interpretability needs. The 'Counterfactuals' component allows users to see what changes would lead to a different prediction, directly addressing the need to 'determine what must be done to get a desirable outcome.' The 'Causal' component uses causal inference techniques to identify features that have a direct causal effect on the outcome, satisfying the requirement to 'identify the features that have the most direct effect.'Why the Other Options Are Wrong
'Error Analysis' helps identify where the model performs poorly but does not provide actionable steps for individual predictions or causal links. 'Explanation' provides global and local feature importance scores but relies on correlation rather than causation; therefore, it cannot definitively claim a feature has the 'most direct effect' in a causal sense, which is a stricter requirement than general importance.Community Comment Notes
Comment [1] correctly cites Microsoft documentation distinguishing between counterfactuals for user solutions and causal inference for direct effect identification. Comment [3] reinforces that counterfactuals are used for examining fairness and providing solutions to users. Comment [2] highlights the common confusion between Explanation and Causal components, emphasizing the semantic difference in 'direct effect'.Official Reference
Exam Strategy
Focus on the precise definitions of Responsible AI components in Azure ML. Memorize that 'Counterfactuals' = 'What if I change this?' (Actionable), and 'Causal' = 'Direct Impact' (Root Cause), whereas 'Explanation' = 'Relative Importance' (Correlation).