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 perturbs individual input features around a single prediction and fits a sparse linear surrogate, producing a locally faithful, interpretable explanation. This satisfies the stem's requirement for local explanations of one ensemble prediction for a business stakeholder.
- ✗
Model card
Why it's wrong here
A model card is static documentation describing a model's intended use, performance and limitations; it cannot perturb features or fit a surrogate for one prediction. It is tempting because model cards support governance and transparency reporting, and would be correct when stakeholders need an overall summary of a model's behaviour.
- ✗
SHAP
Why it's wrong here
SHAP assigns each feature a Shapley-value contribution for a single prediction, but it derives these from cooperative game theory rather than by perturbing inputs and fitting a surrogate model. It is tempting because SHAP also produces local explanations, and would be correct when additive feature attributions are required.
- ✗
Attention visualisation
Why it's wrong here
Attention visualisation highlights which input tokens a transformer weighted, but it neither perturbs features nor fits a surrogate model, and it does not apply to arbitrary ensembles. It is tempting because attention maps look interpretable for text models, and would be correct when explaining a transformer's token-level focus.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.