- A
LIME
LIME creates a simple, interpretable model around the prediction to explain the decision locally, making it ideal for this task.
- B
SHAP values
Why wrong: SHAP can explain individual predictions, but it is more computationally expensive and may be harder to interpret for non-technical stakeholders; LIME is simpler for a quick local explanation.
- C
Model cards
Why wrong: Model cards provide high-level documentation about the model's intended use and performance, not explanations for individual predictions.
- D
Attention visualization
Why wrong: Attention visualization is used for neural networks with attention mechanisms, not typically for tree-based models.
Question 452 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 specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single 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 is specifically designed to provide local explanations for individual predictions by approximating the complex model with a simpler, interpretable surrogate model around that specific instance. For a tree-based model that is not inherently interpretable, LIME can explain why a single loan application was rejected by perturbing the input and observing the changes in predictions, making it ideal for this use case.
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.
- ✓
LIME
Why this is correct
LIME creates a simple, interpretable model around the prediction to explain the decision locally, making it ideal for this task.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SHAP values
Why it's wrong here
SHAP can explain individual predictions, but it is more computationally expensive and may be harder to interpret for non-technical stakeholders; LIME is simpler for a quick local explanation.
- ✗
Model cards
Why it's wrong here
Model cards provide high-level documentation about the model's intended use and performance, not explanations for individual predictions.
- ✗
Attention visualization
Why it's wrong here
Attention visualization is used for neural networks with attention mechanisms, not typically for tree-based models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between local vs. global interpretability methods, and the trap here is that candidates may choose SHAP values (Option B) because they are also popular for explanations, but SHAP is more suited for global feature importance and can be overkill or less intuitive for a single-instance explanation compared to LIME's direct local surrogate approach.
Detailed technical explanation
How to think about this question
LIME works by generating a local dataset around the instance of interest through random perturbations of the input features, then fitting a sparse linear model (or other interpretable model) weighted by proximity to the original instance. The key subtlety is that the interpretable model's coefficients directly indicate feature importance for that specific prediction, allowing the data scientist to say, for example, 'the loan was rejected primarily because the debt-to-income ratio was too high.' In practice, LIME requires careful selection of the number of perturbations and the kernel width to avoid misleading explanations, especially for high-dimensional or categorical data.
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 choice because it is specifically designed to provide local explanations for individual predictions by approximating the complex model with a simpler, interpretable surrogate model around that specific instance. For a tree-based model that is not inherently interpretable, LIME can explain why a single loan application was rejected by perturbing the input and observing the changes in predictions, making it ideal for this use case.
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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