AI0-001 Implementing AI Solutions Practice Question
A financial services firm runs an AI model that scores loan applications. Regulators require the firm to explain any adverse decision to an applicant. The model is a gradient-boosted tree with hundreds of features. Which implementation approach best satisfies the explainability requirement without replacing the model?
⚠ Common exam trap
A common mix-up: candidates confuse global feature importance with per-applicant explanations, when adverse action notices require reasons tied to the individual decision.
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
✓
Generate per-applicant explanations using SHAP values that attribute the decision to the most influential features.
Adverse action explanations must be specific to the individual applicant, and SHAP values provide locally accurate attributions for each prediction. This preserves the gradient-boosted tree's performance while producing reasons that can be communicated and audited. Global importance describes the population, logistic regression replaces the model, and raw logs record inputs without explaining the decision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the gradient-boosted tree with a logistic regression so coefficients are directly interpretable.
Why it's wrong here
A logistic regression is inherently interpretable, but replacing the model sacrifices predictive performance and requires full revalidation, retraining, and regulatory review. The scenario explicitly asks for an approach without replacing the model. Simpler coefficients also may not capture the nonlinear relationships the tree model learned, so this is a costly and unnecessary trade.
- ✗
Publish the model's global feature importance ranking so applicants can see which factors matter most overall.
Why it's wrong here
Global feature importance describes the model across the whole population, not the reasons for one applicant's decision. Regulators require an explanation of the specific adverse action, and a global ranking cannot show which factors drove this applicant's score. It is directionally useful for model review but insufficient for per-applicant adverse action notices.
- ✓
Generate per-applicant explanations using SHAP values that attribute the decision to the most influential features.
Why this is correct
SHAP values provide consistent, locally accurate feature attributions for individual predictions, which lets the firm explain why a specific applicant was declined. This satisfies the requirement to explain adverse decisions without discarding the high-performing tree model. The attributions can be presented in applicant-facing language and audited by regulators.
- ✗
Log the model's raw input features and output score for each application so auditors can review the data.
Why it's wrong here
Logging inputs and scores supports auditability but does not explain why the model produced a particular decision. An applicant receiving an adverse action needs the reasons, not a record of the inputs. Raw feature logs also may contain sensitive data and do not translate into understandable factors, so this does not satisfy the explainability requirement.
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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
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