AI0-001 AI Concepts and Foundations Practice Question
A financial services firm is deploying a credit-scoring model that uses alternative data such as utility payments and rental history. The model shows high accuracy but the firm is concerned about regulatory compliance and explainability. The firm must provide adverse action notices to applicants who are denied credit. Which approach best satisfies the need for explainability while maintaining model performance?
⚠ Common exam trap
The trap here is assuming that global feature importance or general disclosures are sufficient for per-applicant adverse action notices, when regulations require specific reasons for each denial.
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
✓
Use a gradient boosting model with SHAP values to generate per-applicant reason codes for denials.
Gradient boosting with SHAP values is the best choice because SHAP provides per-applicant, locally faithful explanations that can be directly used in adverse action notices. It preserves the model's ability to leverage alternative data for accuracy while delivering the transparency regulators demand. Other options either sacrifice performance, fail to provide specific reasons, or rely on approximations that may not be reliable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a black-box deep learning model and provide only the top-level feature importances from a surrogate model.
Why it's wrong here
Surrogate models approximate the black-box but may not faithfully represent its decisions, leading to inaccurate or misleading explanations. Regulatory adverse action notices require specific reasons for denial, not just global feature importance. This approach risks non-compliance because the explanations may not reflect the actual decision logic for each applicant.
- ✗
Replace the model with a simple logistic regression using only traditional credit bureau data.
Why it's wrong here
Switching to a simpler model may improve explainability but sacrifices the predictive power gained from alternative data. The firm likely wants to retain the performance benefits of alternative data. This option does not address how to explain a model that uses alternative data; it merely avoids the problem by discarding useful information.
- ✗
Deploy the model as a black box but disclose the general factors that influence credit decisions in the application terms.
Why it's wrong here
General disclosures do not satisfy the requirement to provide specific reasons for an individual's denial. Regulations such as the Equal Credit Opportunity Act require adverse action notices to state the principal reasons for the decision. This approach lacks the per-applicant specificity needed for compliance.
- ✓
Use a gradient boosting model with SHAP values to generate per-applicant reason codes for denials.
Why this is correct
SHAP values provide consistent, locally accurate feature attributions for each prediction, which can be translated into reason codes for adverse action notices. Gradient boosting maintains high performance with alternative data. This combination balances explainability and accuracy, meeting regulatory requirements without sacrificing predictive capability.
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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.