AI0-001 AI Governance and Ethics Practice Question
A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?
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
✓
LIME
LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by perturbing inputs and learning a linear surrogate. SHAP provides Shapley values, which are also local but game-theoretic. Attention visualisation is for transformer models. Model cards describe global model behaviour.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
LIME
Why this is correct
LIME generates local explanations by perturbing the input, observing changes in predictions, and fitting a simple interpretable model (e.g., linear) around the instance.
- ✗
Model card
Why it's wrong here
A model card is a static document describing overall model characteristics, not a per-instance explanation tool.
- ✗
SHAP
Why it's wrong here
SHAP uses Shapley values from cooperative game theory to explain predictions, but it is not specifically a perturb-then-fit surrogate method like LIME.
- ✗
Attention visualisation
Why it's wrong here
Attention visualisation shows which parts of the input the model focused on, but it is model-specific and not a generic surrogate method.
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Written by Johnson Ajibi, MSc IT Security
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This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.