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?
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
Glass-box models like logistic regression or decision trees are inherently interpretable and can provide clear, auditable reasons for each prediction. Black-box models require post-hoc explainability methods, which may not be sufficient for regulatory scrutiny.
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 are transparent by design and provide direct insight into decision logic, satisfying regulatory demands.
- ✗
A gradient-boosted tree ensemble with SHAP explanations
Why it's wrong here
While SHAP improves interpretability, ensemble models are still considered black-box and may not meet strict regulatory requirements.
- ✗
A black-box model with a model card describing its behavior
Why it's wrong here
Model cards describe overall performance but do not provide per-decision explanations required by regulators.
- ✗
A deep neural network with LIME explanations
Why it's wrong here
Deep neural networks are black-box; LIME provides approximations but may not be fully reliable for regulatory purposes.
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.