AI0-001 AI Governance and Ethics Practice Question
A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?
⚠ Common exam trap
AI0-001 often tests whether candidates equate post-hoc explanation tools (SHAP, LIME) with true interpretability — regulators in high-stakes domains typically require inherently interpretable models, not approximations.
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 glass-box model such as logistic regression or a decision tree
A glass-box model such as logistic regression or a decision tree is inherently interpretable, meaning its internal decision logic can be directly examined and explained to regulators without post-hoc approximation. For credit scoring, regulators often require clear, auditable reasons for each decision (e.g., adverse action notices under ECOA/Regulation B), and glass-box models provide this natively. This makes them the correct choice when interpretability is a hard requirement.
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 glass-box model such as logistic regression or a decision tree
Why this is correct
Glass-box models such as logistic regression and decision trees expose their internal decision logic, so each credit decision can be explained directly to regulators. This satisfies the interpretability constraint, unlike opaque neural networks or ensemble methods whose reasoning cannot be clearly justified.
- ✗
A gradient-boosted tree ensemble with SHAP explanations
Why it's wrong here
SHAP values explain a tree ensemble's output after training, but the ensemble itself remains a black box whose splits and interactions regulators cannot inspect directly. It tempts because SHAP is the standard tool when high predictive accuracy outweighs the need for a self-explanatory model.
- ✗
A black-box model with a model card describing its behavior
Why it's wrong here
A black-box model with a model card documents behaviour but cannot explain individual credit decisions, failing the regulator's requirement for per-decision reasons. Model cards suit governance and disclosure scenarios; interpretable models such as logistic regression or decision trees provide the actual reasoning regulators demand.
- ✗
A deep neural network with LIME explanations
Why it's wrong here
LIME approximates a black-box model locally, so explanations are post-hoc estimates rather than the model's own decision logic; regulators requiring defensible per-decision reasons accept this poorly. It tempts because LIME suits auditing complex models where no inherently interpretable alternative reaches the required accuracy.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.