AI0-001 AI Governance and Ethics Practice Question
A data scientist is using SHAP to explain a complex ensemble model's predictions. A business stakeholder asks why a particular prediction was made. The data scientist wants to show the most influential features for that single prediction. Which SHAP visualisation is most appropriate?
⚠ Common exam trap
CompTIA often tests the distinction between global vs. local interpretability, and the trap here is that candidates confuse summary plots (global) or dependence plots (global) with force plots (local), leading them to choose a globally-focused visualization for a single-prediction explanation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A SHAP force plot for the individual prediction
A SHAP force plot is specifically designed to visualize the contribution of each feature to a single prediction, showing how features push the prediction from the base value (average model output) to the final prediction. This makes it the ideal choice for explaining an individual prediction to a business stakeholder, as it provides a clear, localized explanation of feature impacts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A SHAP summary plot showing mean absolute SHAP values across all features
Why it's wrong here
A summary plot aggregates mean absolute SHAP values across the whole dataset, describing global feature importance rather than one prediction. It would be the right choice when explaining overall model behaviour to stakeholders. For a single prediction, a local force or waterfall plot showing that instance's feature contributions is needed.
- ✗
A SHAP dependence plot for the top feature
Why it's wrong here
A dependence plot shows how one feature's SHAP value varies with its value across many instances, revealing global relationships and interactions. It answers how a feature influences predictions generally. Explaining a single prediction needs a local waterfall or force plot covering all contributing features for that instance.
- ✗
A SHAP bar chart of absolute feature importance
Why it's wrong here
A bar chart of absolute feature importance again aggregates magnitude across all instances, giving a global ranking with no direction or per-instance detail. It suits reporting which features matter overall. Explaining one prediction requires a local plot, such as a waterfall, that decomposes that instance's SHAP values.
- ✓
A SHAP force plot for the individual prediction
Why this is correct
A force plot displays the push and pull each feature exerts on one specific prediction, showing magnitude and direction for that single case. It satisfies the stakeholder's request for the most influential features behind an individual outcome, unlike global summary plots.
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