Understanding LIME and SHAP for Local Explanations
A team is deploying a regression model for loan approval. To ensure transparency for regulators, they need to explain individual predictions. Which interpretability method can provide local explanations by approximating the model with a simpler surrogate?
Quick Answer
The correct answer is LIME. LIME, which stands for Local Interpretable Model-agnostic Explanations, works by approximating a complex model with a simpler, interpretable surrogate model around a single prediction, making it ideal for explaining individual loan approval decisions to regulators. This directly addresses the need for local explainability, whereas SHAP also provides local explanations but relies on game-theoretic Shapley values, and both partial dependence and permutation importance offer global rather than local insights. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to distinguish between local and global interpretability methods, with a common trap being to confuse LIME and SHAP since both are local—remember that LIME uses a simpler surrogate model while SHAP uses additive feature attribution. A helpful memory tip: LIME is like a “local spotlight” that shines on one prediction at a time, while SHAP is more like a “fair calculator” that divides credit among all features.
⚠ Common exam trap
It's easy for candidates to confuse SHAP values (which also provide local explanations) with LIME, but SHAP does not use a simpler surrogate model—it directly computes feature attributions from the original model, which is a key distinction the exam tests.
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 explanations by fitting a simpler, interpretable surrogate model (e.g., linear regression or decision tree) around a single prediction. This allows the team to explain why a specific loan application was approved or rejected, meeting regulatory transparency requirements without needing access to the original model's internals.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SHAP values
Why it's wrong here
SHAP is a local method but based on cooperative game theory, not surrogate models.
- ✗
Partial dependence plots
Why it's wrong here
Partial dependence is a global interpretability method.
- ✓
LIME
Why this is correct
LIME creates local surrogate models to explain individual predictions.
- ✗
Permutation feature importance
Why it's wrong here
Permutation importance is a global method that measures feature importance by shuffling.
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Same concept, more angles
1 more way this is tested on AIF-C01
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 research lab uses Amazon SageMaker to train a deep learning model for medical diagnosis. They need to ensure the model's decisions are interpretable to clinicians. Which SageMaker feature provides local and global feature importance?
hard- A.SageMaker Model Monitor
- B.SageMaker Experiments
- ✓ C.SageMaker Clarify
- D.SageMaker Debugger
Why C: SageMaker Clarify is the correct answer because it is specifically designed to provide both local and global feature importance for machine learning models. Local feature importance explains individual predictions (e.g., why a specific patient was diagnosed), while global feature importance shows which features most influence the model overall. This directly supports interpretability for clinicians, as required in the question.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.