AI0-001 AI Governance and Ethics Practice Question
A data scientist needs to explain why a black-box model denied a loan application. Which explainability technique generates local feature importance values using a simpler interpretable model around the prediction?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between local vs. global explainability methods, and the trap here is that candidates may confuse SHAP values (which also provide local feature importance) with LIME, failing to recognize that LIME uniquely uses a simpler interpretable surrogate model trained around the prediction, while SHAP uses game-theoretic contributions without a 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) is the correct technique because it generates local feature importance values by fitting a simpler, interpretable model (e.g., linear regression or decision tree) around the prediction of the black-box model. This allows the data scientist to explain why a specific loan application was denied by identifying which features (e.g., income, credit score) most influenced that particular decision. Unlike global methods, LIME focuses on the local neighborhood of the instance, making it ideal for explaining individual predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Model card
Why it's wrong here
Model cards provide high-level documentation, not local explanations.
- ✓
LIME
Why this is correct
LIME fits a simple model (e.g., linear) locally to approximate the black-box model's decision boundary for a specific instance.
- ✗
Attention visualisation
Why it's wrong here
Attention visualisation is specific to transformer models and not model-agnostic.
- ✗
SHAP values
Why it's wrong here
SHAP provides global and local feature importance but uses Shapley values, not a simpler model.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.