- A
Attention visualization
Why wrong: Attention mechanisms are specific to transformer models and may not apply to other architectures.
- B
Model cards
Why wrong: Model cards document overall model behavior, not individual predictions.
- C
LIME
LIME is designed specifically for local, interpretable explanations of any classifier.
- D
SHAP values
Why wrong: SHAP provides global and local feature importance based on game theory, but LIME is more commonly used for local explanations.
Question 985 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.
An insurance company uses a black-box deep learning model to set premiums. Regulators demand explanation for individual decisions. Which interpretability technique should the data science team apply to generate local explanations for each 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 choice because it generates local, interpretable explanations for individual predictions by approximating the black-box model with a simpler, interpretable surrogate model around the specific instance. This directly meets the regulatory requirement for explaining why a particular premium was set for a specific customer, without needing access to the model's internal structure.
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.
- ✗
Attention visualization
Why it's wrong here
Attention mechanisms are specific to transformer models and may not apply to other architectures.
- ✗
Model cards
Why it's wrong here
Model cards document overall model behavior, not individual predictions.
- ✓
LIME
Why this is correct
LIME is designed specifically for local, interpretable explanations of any classifier.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SHAP values
Why it's wrong here
SHAP provides global and local feature importance based on game theory, but LIME is more commonly used for local explanations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SHAP and LIME as both being local explainers, but Cisco tests the nuance that LIME is model-agnostic and simpler to implement for deep learning black-boxes, while SHAP assumes a specific game-theoretic framework that may not align with the model's actual behavior.
Detailed technical explanation
How to think about this question
LIME works by generating a synthetic dataset of perturbed samples around the instance to be explained, then fitting a sparse linear model (e.g., Lasso) weighted by proximity to the original instance. The key subtlety is that the choice of perturbation function (e.g., sampling from a normal distribution for continuous features) and the kernel width for weighting can significantly affect the stability and faithfulness of the explanation, which is critical when regulators demand consistent justifications across similar cases.
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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AI Governance and Ethics — study guide chapter
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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 choice because it generates local, interpretable explanations for individual predictions by approximating the black-box model with a simpler, interpretable surrogate model around the specific instance. This directly meets the regulatory requirement for explaining why a particular premium was set for a specific customer, without needing access to the model's internal structure.
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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