- A
Model card
Why wrong: Model cards provide high-level documentation, not local explanations.
- B
LIME
LIME fits a simple model (e.g., linear) locally to approximate the black-box model's decision boundary for a specific instance.
- C
Attention visualisation
Why wrong: Attention visualisation is specific to transformer models and not model-agnostic.
- D
SHAP values
Why wrong: SHAP provides global and local feature importance but uses Shapley values, not a simpler model.
Question 388 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 data scientist needs to explain why a black-box model denied a loan application. Which explainability technique generates local feature importance values using a simpler interpretable model around the 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 feature importance values by fitting a simpler, interpretable model (e.g., linear regression or decision tree) around the prediction of the black-box model. This allows the data scientist to explain why a specific loan application was denied by identifying which features (e.g., income, credit score) most influenced that particular decision. Unlike global methods, LIME focuses on the local neighborhood of the instance, making it ideal for explaining individual predictions.
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.
- ✗
Model card
Why it's wrong here
Model cards provide high-level documentation, not local explanations.
- ✓
LIME
Why this is correct
LIME fits a simple model (e.g., linear) locally to approximate the black-box model's decision boundary for a specific instance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Attention visualisation
Why it's wrong here
Attention visualisation is specific to transformer models and not model-agnostic.
- ✗
SHAP values
Why it's wrong here
SHAP provides global and local feature importance but uses Shapley values, not a simpler model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between local vs. global explainability methods, and the trap here is that candidates may confuse SHAP values (which also provide local feature importance) with LIME, failing to recognize that LIME uniquely uses a simpler interpretable surrogate model trained around the prediction, while SHAP uses game-theoretic contributions without a surrogate model.
Detailed technical explanation
How to think about this question
LIME works by generating perturbed samples around the instance of interest, weighting them by proximity (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 perturbed samples can significantly affect the explanation stability; too narrow a kernel may overfit to noise, while too wide a kernel may lose locality. In a real-world loan denial scenario, LIME can reveal that a high debt-to-income ratio was the primary driver, even if the global model relies heavily on credit history, enabling the data scientist to provide a transparent, actionable reason to the applicant.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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 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 feature importance values by fitting a simpler, interpretable model (e.g., linear regression or decision tree) around the prediction of the black-box model. This allows the data scientist to explain why a specific loan application was denied by identifying which features (e.g., income, credit score) most influenced that particular decision. Unlike global methods, LIME focuses on the local neighborhood of the instance, making it ideal for explaining individual predictions.
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
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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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