- A
SHAP values
Why wrong: SHAP values provide local explanations based on Shapley values, but they are not surrogate models; they compute feature contributions directly.
- B
LIME
LIME trains a local surrogate model (e.g., linear model) to approximate the complex model's behavior near a specific prediction.
- C
Attention visualisation
Why wrong: Attention visualisation is used in transformer models but is not a general-purpose model-agnostic explainability technique.
- D
Model cards
Why wrong: Model cards are transparency documentation, not a technique for explaining individual predictions.
Question 13 of 1,000
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. 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.
A healthcare AI startup is developing a model to predict patient readmission risk. The company wants to ensure the model's decisions can be understood by clinicians. Which explainability technique provides local, model-agnostic explanations by fitting a simple surrogate model around a prediction?
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
✓
LIME
LIME (Local Interpretable Model-agnostic Explanations) is the correct technique because it generates local explanations by fitting a simple, interpretable surrogate model (e.g., linear regression or decision tree) around a specific prediction. It is model-agnostic, meaning it works with any black-box classifier, and it perturbs the input data near the instance of interest to understand which features most influenced the prediction.
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.
- ✗
SHAP values
Why it's wrong here
SHAP values provide local explanations based on Shapley values, but they are not surrogate models; they compute feature contributions directly.
- ✓
LIME
Why this is correct
LIME trains a local surrogate model (e.g., linear model) to approximate the complex model's behavior near a specific prediction.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Attention visualisation
Why it's wrong here
Attention visualisation is used in transformer models but is not a general-purpose model-agnostic explainability technique.
- ✗
Model cards
Why it's wrong here
Model cards are transparency documentation, not a technique for explaining individual predictions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between local vs. global explanations and model-agnostic vs. model-specific techniques, so candidates may confuse SHAP (which is also local and model-agnostic) with LIME because both provide feature importance, but SHAP does not use a surrogate model.
Detailed technical explanation
How to think about this question
Under the hood, LIME generates a synthetic dataset by sampling perturbations around the input instance, weighting them by proximity (e.g., using an exponential kernel), and then training a sparse linear model on these weighted samples to approximate the black-box model's behavior locally. A subtle behavior is that the choice of kernel width and number of perturbations can significantly affect the stability and fidelity of the explanation, making it sensitive to hyperparameter tuning. In a real-world clinical readmission risk scenario, LIME might highlight that a specific patient's high creatinine level and age were the top two drivers for a high-risk prediction, allowing a clinician to quickly verify or challenge the model's reasoning.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: LIME — LIME (Local Interpretable Model-agnostic Explanations) is the correct technique because it generates local explanations by fitting a simple, interpretable surrogate model (e.g., linear regression or decision tree) around a specific prediction. It is model-agnostic, meaning it works with any black-box classifier, and it perturbs the input data near the instance of interest to understand which features most influenced the prediction.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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