- A
Generate SHAP (SHapley Additive exPlanations) values
SHAP values explain the contribution of each feature to predictions.
- B
Use differential privacy to add noise to training data
Why wrong: Differential privacy protects privacy, not interpretability.
- C
Implement a random forest algorithm
Why wrong: Random forests are algorithms, not interpretability techniques.
- D
Use deep neural networks to increase model complexity
Why wrong: Deep neural networks reduce transparency.
- E
Apply LIME (Local Interpretable Model-agnostic Explanations)
LIME approximates the model locally to provide explanations.
Question 498 of 1,000
AI Interpretability Techniques: SHAP and LIME
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are common techniques to improve the transparency and interpretability of an AI model?
Quick Answer
The answer is SHAP and LIME, as these are the two common techniques to improve the transparency and interpretability of an AI model. Both are model-agnostic interpretability methods, meaning they can explain predictions from any machine learning model without needing access to its internal structure. SHAP values use game theory to assign each feature a contribution score for a specific prediction, while LIME builds a simple, local surrogate model around that prediction to approximate the black box’s behavior. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish interpretability tools from model types or unrelated concepts—a common trap is confusing a specific algorithm like random forest with a general explanation technique. Remember the memory tip: SHAP and LIME are the “explainers,” not the models themselves.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
SHAP values work by computing the average marginal contribution of a feature across all possible feature subsets, which satisfies properties like local accuracy and consistency, making them theoretically grounded for model explanation. LIME (Local Interpretable Model-agnostic Explanations) approximates the model locally with a simpler, interpretable surrogate model (e.g., linear regression) by perturbing input instances and observing output changes, which is computationally efficient but can be sensitive to the perturbation distribution. In real-world scenarios, such as credit scoring or medical diagnosis, these techniques are mandated by regulations like GDPR's right to explanation, where SHAP and LIME help auditors verify that models are not making biased or opaque decisions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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