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
Option A is correct because SHAP (SHapley Additive exPlanations) values, grounded in cooperative game theory, assign each feature a quantitative contribution to a prediction, providing both global and local interpretability for any model. Option E is correct because LIME (Local Interpretable Model-agnostic Explanations) approximates a complex model's behavior around a single prediction with a simple, interpretable surrogate model, exposing which features drove that decision. Both techniques are model-agnostic post-hoc explanation methods specifically designed to make AI outputs transparent and interpretable. Option B is not a transparency technique: differential privacy adds calibrated noise to protect individual records, trading accuracy for privacy rather than explaining model behavior. Option C is not inherently an interpretability technique; random forests are ensembles whose many trees are typically less transparent than a single decision tree. Option D is incorrect because increasing complexity with deep neural networks generally reduces interpretability rather than improving it.
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 quantify each feature's contribution to a prediction using Shapley values from cooperative game theory, producing consistent local and global explanations. This directly satisfies the stem's requirement for a technique improving transparency and interpretability of an AI model.
- ✗
Use differential privacy to add noise to training data
Why it's wrong here
Differential privacy adds calibrated noise to protect individual records, which degrades accuracy and obscures how inputs drive outputs, working against interpretability. It is tempting because it is a genuine responsible-AI technique, and would be the right choice when the requirement is privacy preservation during training rather than explaining model behaviour.
- ✗
Implement a random forest algorithm
Why it's wrong here
A random forest is an ensemble of decision trees whose aggregated votes are not directly readable, so it does not itself improve transparency. It is tempting because individual trees are interpretable and it is a strong general-purpose classifier, and would be correct when the goal is predictive accuracy on tabular data rather than explainability.
- ✗
Use deep neural networks to increase model complexity
Why it's wrong here
Deep neural networks stack many non-linear layers, producing millions of interacting weights that resist direct human interpretation. It is tempting because added depth often raises predictive performance on complex data, and would be the right choice when the requirement is accuracy on high-dimensional tasks such as image or speech recognition.
- ✓
Apply LIME (Local Interpretable Model-agnostic Explanations)
Why this is correct
LIME fits a local surrogate model around each prediction to approximate how individual features influence the output, yielding model-agnostic explanations. This satisfies the stem's requirement for a technique that improves transparency and interpretability of an AI model.
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.