AI0-001 AI Governance and Ethics Practice Question
A data scientist needs to explain why a specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single prediction?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between local vs. global interpretability methods, and the trap here is that candidates may choose SHAP values (Option B) because they are also popular for explanations, but SHAP is more suited for global feature importance and can be overkill or less intuitive for a single-instance explanation compared to LIME's direct local surrogate approach.
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 choice because it is specifically designed to provide local explanations for individual predictions by approximating the complex model with a simpler, interpretable surrogate model around that specific instance. For a tree-based model that is not inherently interpretable, LIME can explain why a single loan application was rejected by perturbing the input and observing the changes in predictions, making it ideal for this use case.
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 creates a simple, interpretable model around the prediction to explain the decision locally, making it ideal for this task.
- ✗
SHAP values
Why it's wrong here
SHAP values are tempting because they provide excellent local explanations, quantifying each feature's contribution to a single prediction, and are widely applicable to complex models, including tree ensembles. However, for a *single* tree-based model prediction, SHAP values explain *what* features influenced the outcome but do not explicitly trace the *decision path* through the tree structure that led to that specific prediction. The question implies a need for an explanation that directly leverages the model's internal logic for that specific instance.
- ✗
Model cards
Why it's wrong here
Model cards provide high-level documentation about the model's intended use and performance, not explanations for individual predictions.
- ✗
Attention visualization
Why it's wrong here
Attention visualization is used for neural networks with attention mechanisms, not typically for tree-based models.
About these practice questions
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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.