AI0-001 AI Governance and Ethics Practice Question
A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?
⚠ Common exam trap
AI0-001 often tests the distinction between local and global explanation methods, and candidates may mistakenly think LIME provides global feature importance or requires model internals.
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
✓
It creates an interpretable surrogate model locally around a prediction
Option D is correct because LIME (Local Interpretable Model-agnostic Explanations) works by perturbing the input around a specific prediction and fitting a simple, interpretable surrogate model (such as a linear model or decision tree) that approximates the black-box model's behavior in that local neighborhood. Option E is correct because LIME is model-agnostic: it only requires the ability to query the model's predictions (input-output access), so it can be applied to any machine learning model, including neural networks, SVMs, and gradient-boosted trees. Option A is not correct because LIME explains which features drove a prediction, not the model's confidence or probability calibration. Option B is not correct because LIME treats the model as a black box and does not need internal parameters or gradients. Option C is not correct because LIME produces local explanations for individual predictions, not a global feature-importance ranking across the entire dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It provides a measure of model confidence in its prediction
Why it's wrong here
LIME reports which features pushed a single prediction, not a calibrated probability or confidence score; the surrogate's fit quality is separate from model certainty. Confidence measures come from softmax outputs or calibration methods, which is why LIME suits explaining reasoning rather than quantifying certainty.
- ✗
It requires access to the model's internal parameters
Why it's wrong here
LIME approximates the black box by querying inputs and observing outputs, so it never reads weights or internal layers. That model-agnostic querying is precisely why it suits opaque or third-party models; requiring parameter access describes intrinsic methods such as SHAP on tree ensembles or gradient-based saliency.
- ✗
It provides a global ranking of feature importance across the entire dataset
Why it's wrong here
LIME fits a local surrogate around one prediction, so its feature weights describe that instance rather than the whole dataset. Global ranking is what SHAP's aggregated values or permutation importance deliver; LIME is chosen when a single decision needs explaining to a reviewer.
- ✓
It creates an interpretable surrogate model locally around a prediction
Why this is correct
LIME fits an interpretable surrogate model, such as a sparse linear model, on samples generated locally around the instance being explained. This satisfies the stem's constraint because the approximation is faithful only near that prediction, not globally across the model.
- ✓
It can be used with any machine learning model
Why this is correct
LIME is model-agnostic: it perturbs inputs and observes outputs, so it works with any machine learning model, including black boxes. This satisfies the stem's constraint because no access to internal weights or architecture is required to generate explanations.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.