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AI Security, Ethics and GovernanceeasyMultiple SelectObjective-mapped

AI0-001 AI Security, Ethics and Governance Practice Question

Which TWO of the following are common techniques to improve the transparency and interpretability of an AI model?

⚠ Common exam trap

The AI0-001 exam often tests the distinction between techniques that improve model transparency (like SHAP and LIME) versus techniques that enhance privacy (like differential privacy) or model performance (like random forests or deep neural networks), leading candidates to confuse privacy-preserving methods with interpretability methods.

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 SHAP (SHapley Additive exPlanations) values

SHAP values are correct because they provide a unified measure of feature importance based on cooperative game theory, specifically Shapley values, which quantify the marginal contribution of each feature to a model's prediction. This makes the model's decision-making process transparent by showing how each input feature influences the output, which is a core technique for interpretability in AI governance.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Generate SHAP (SHapley Additive exPlanations) values

    Why this is correct

    SHAP values explain the contribution of each feature to predictions.

  • Use differential privacy to add noise to training data

    Why it's wrong here

    Differential privacy protects privacy, not interpretability.

  • Implement a random forest algorithm

    Why it's wrong here

    Random forests are algorithms, not interpretability techniques.

  • Use deep neural networks to increase model complexity

    Why it's wrong here

    Deep neural networks reduce transparency.

  • Apply LIME (Local Interpretable Model-agnostic Explanations)

    Why this is correct

    LIME approximates the model locally to provide explanations.

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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.