AI0-001 AI Infrastructure and Technologies Practice Question
A financial institution is deploying an AI model for credit scoring. The model must be explainable to regulators, and the team needs to understand which features contribute most to individual predictions. Which TWO techniques should they use? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse global feature importance methods with local explanation techniques, which are required for individual prediction transparency.
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
✓
SHAP (SHapley Additive exPlanations)
For explaining individual predictions in a credit scoring model, local explanation techniques are necessary. SHAP and LIME both provide per-instance feature importance, which can be presented to regulators to justify decisions. SHAP offers a solid theoretical foundation, while LIME is flexible and model-agnostic. Together, they cover the need for explainability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique that transforms features into uncorrelated components. While it can help with visualization, it does not provide interpretable feature importance for individual predictions. The components are combinations of original features, making it difficult to explain decisions to regulators.
- ✓
SHAP (SHapley Additive exPlanations)
Why this is correct
SHAP is a game-theoretic approach that assigns each feature an importance value for a particular prediction. It provides consistent and locally accurate explanations, which are essential for regulatory compliance in credit scoring. SHAP values can be visualized to show how each feature pushes the prediction from the base value.
- ✓
LIME (Local Interpretable Model-agnostic Explanations)
Why this is correct
LIME explains individual predictions by approximating the model locally with an interpretable surrogate model. It is model-agnostic and can be used for any classifier, making it suitable for explaining credit scoring decisions to regulators. It highlights which features were most influential for a specific instance.
- ✗
Feature importance from a random forest
Why it's wrong here
Global feature importance from a random forest provides an overall ranking of features but does not explain individual predictions. Regulators often require instance-level explanations, so this technique alone is insufficient. It can be part of the explanation but not the primary method.
- ✗
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Why it's wrong here
t-SNE is a nonlinear dimensionality reduction method primarily used for visualizing high-dimensional data in 2D or 3D. It does not offer per-instance feature attributions and is not suitable for explaining individual credit scoring decisions. It is more for exploratory data analysis.
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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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