- A
Replace the model with a decision tree for transparency
Why wrong: May not be feasible if deep learning is required.
- B
Use SHAP values to understand feature contributions
SHAP provides consistent and theoretically grounded explanations.
- C
Rely on the model's internal attention weights (if transformer-based)
Why wrong: Attention weights are not always reliable explanations.
- D
Apply LIME to generate local explanations for each prediction
LIME approximates model behavior locally.
- E
Calculate global feature importance using permutation importance
Why wrong: Global importance doesn't explain individual decisions.
Question 113 of 1,000
Using SHAP and LIME for AI Explainability
This AI0-001 practice question tests your understanding of ai implementation and operations. 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 techniques are most effective for ensuring model explainability in a production loan approval AI system subject to regulatory review? (Select TWO.)
Quick Answer
The correct answer is to apply LIME for local explanations and SHAP for both global and local feature contributions. LIME, or Local Interpretable Model-agnostic Explanations, works by perturbing input data around a single prediction to build a simple, interpretable surrogate model, making it ideal for explaining why a specific loan application was approved or denied. SHAP, or SHapley Additive exPlanations, uses game theory to assign each feature a contribution value, offering a consistent and mathematically grounded view of how features impact predictions both for individual cases and across the entire dataset. On the CompTIA AI+ AI0-001 exam, this question tests your understanding that regulatory review demands per-instance transparency, not just global feature importance—a common trap is choosing feature importance alone, which lacks local granularity. Remember the mnemonic: “LIME lights the local path, SHAP shares the full map.”
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
✓
Use SHAP values to understand feature contributions
The correct answers are B (SHAP values) and D (LIME). SHAP values provide both global and local feature importance, satisfying explainability for regulatory review. LIME generates local explanations for individual predictions, which is crucial for understanding specific loan decisions. Option A is incorrect because replacing the model with a decision tree may not be feasible or maintain performance. Option C is incorrect because attention weights are not reliable or standardized for explainability. Option E is incorrect because permutation importance offers global importance but lacks local explanations.
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.
- ✗
Replace the model with a decision tree for transparency
Why it's wrong here
May not be feasible if deep learning is required.
- ✓
Use SHAP values to understand feature contributions
Why this is correct
SHAP provides consistent and theoretically grounded explanations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Rely on the model's internal attention weights (if transformer-based)
Why it's wrong here
Attention weights are not always reliable explanations.
- ✓
Apply LIME to generate local explanations for each prediction
Why this is correct
LIME approximates model behavior locally.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Calculate global feature importance using permutation importance
Why it's wrong here
Global importance doesn't explain individual decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
AI Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
- →
AI Implementation and Operations practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
1,000 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Infrastructure and Technologies practice questions
Practise AI0-001 questions linked to AI Infrastructure and Technologies.
AI Security practice questions
Practise AI0-001 questions linked to AI Security.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
AI Concepts and Techniques practice questions
Practise AI0-001 questions linked to AI Concepts and Techniques.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
Implementing AI Solutions practice questions
Practise AI0-001 questions linked to Implementing AI Solutions.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
AI Governance and Ethics practice questions
Practise AI0-001 questions linked to AI Governance and Ethics.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Implementation and Operations — This question tests AI Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SHAP values to understand feature contributions — The correct answers are B (SHAP values) and D (LIME). SHAP values provide both global and local feature importance, satisfying explainability for regulatory review. LIME generates local explanations for individual predictions, which is crucial for understanding specific loan decisions. Option A is incorrect because replacing the model with a decision tree may not be feasible or maintain performance. Option C is incorrect because attention weights are not reliable or standardized for explainability. Option E is incorrect because permutation importance offers global importance but lacks local explanations.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
Same concept, more angles
1 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare AI system that diagnoses medical images must provide explanations for its predictions to comply with regulatory requirements. Which technique should the team implement?
medium- A.Reduce the model's accuracy to make it simpler.
- B.Only deploy rule-based systems.
- ✓ C.Apply model interpretability methods such as SHAP or LIME.
- D.Use a more complex deep learning model.
Why C: Option C is correct because SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are established model interpretability techniques that provide per-prediction explanations, which are essential for regulatory compliance in healthcare AI. These methods generate feature attribution scores or local surrogate models to explain why a specific diagnosis was made, meeting transparency requirements without sacrificing model performance.
Keep practising
More AI0-001 practice questions
- A data science team uses a CI/CD pipeline for ML models. They need to ensure that each model version is traceable back t…
- A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonse…
- A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model…
- A team is designing an AI system for autonomous driving. They need to decide between an end-to-end deep learning approac…
- A team is using Kubeflow to orchestrate ML workflows on Kubernetes. They need to ensure reproducibility, track experimen…
- A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, t…
Last reviewed: Jun 23, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.