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.
This AIF-C01 practice question tests your understanding of guidelines for responsible ai. 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 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?
SHAP values
Why wrong: SHAP is a local method but based on cooperative game theory, not surrogate models.
Partial dependence plots
Why wrong: Partial dependence is a global interpretability method.
LIME
LIME creates local surrogate models to explain individual predictions.
Permutation feature importance
Why wrong: Permutation importance is a global method that measures feature importance by shuffling.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
LIME
Option C is correct because LIME (Local Interpretable Model-agnostic Explanations) generates local explanations by fitting a simpler model around each prediction. SHAP (option A) is also local but uses Shapley values; partial dependence (option B) is global; permutation importance (option D) is global feature importance.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
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.
Related concept
Static NAT maps one inside address to one outside address.
Permutation feature importance
Why it's wrong here
Permutation importance is a global method that measures feature importance by shuffling.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Guidelines for Responsible AI — study guide chapter
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Guidelines for Responsible AI practice questions
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FAQ
Guidelines for Responsible AI — This question tests Guidelines for Responsible AI — Static NAT maps one inside address to one outside address..
The correct answer is: LIME — Option C is correct because LIME (Local Interpretable Model-agnostic Explanations) generates local explanations by fitting a simpler model around each prediction. SHAP (option A) is also local but uses Shapley values; partial dependence (option B) is global; permutation importance (option D) is global feature importance.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Same concept, more angles
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?
hardWhy C: Option D is correct: SageMaker Clarify provides SHAP-based explanations for feature importance. Option A is wrong: Debugger is for debugging training. Option B is wrong: Model Monitor is for monitoring. Option C is wrong: Experiments is for managing trials.
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Last reviewed: Jun 23, 2026
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.
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