AIF-C01 Guidelines for Responsible AI Practice Question
A data scientist is using Amazon SageMaker to train a model and wants to understand the contribution of each feature to individual predictions. Which technique should they use to generate local explanations?
⚠ Common exam trap
It's easy for candidates to confuse global feature importance (e.g., permutation importance) with local explanation methods, mistakenly thinking that a global ranking can explain individual predictions, when in fact only techniques like SHAP or LIME provide per-instance feature contributions.
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
✓
SHAP values
SHAP (SHapley Additive exPlanations) values are the correct choice because they provide local explanations by decomposing a prediction into the additive contribution of each feature, based on cooperative game theory. This allows the data scientist to understand exactly how each feature influenced a specific individual prediction, unlike global methods that summarize behavior 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.
- ✗
Permutation feature importance
Why it's wrong here
Permutation feature importance measures a feature's average effect on model performance across the whole dataset, producing global rather than per-prediction insight. It is tempting because it is model-agnostic and simple to compute, but local explanations for individual predictions require SHAP or SageMaker Clarify's local attribution.
- ✗
Global feature importance
Why it's wrong here
Global feature importance aggregates average impact across the whole dataset, so it cannot attribute a single prediction to its features. It suits model-level interpretation, such as ranking which inputs drive overall behaviour. Local explanations require per-instance techniques like SHAP or SageMaker Clarify's local attribution.
- ✓
SHAP values
Why this is correct
SHAP values derive from cooperative game theory, assigning each feature a contribution to a single prediction while accounting for feature interactions. This yields consistent, locally accurate explanations per instance, unlike global impurity-based importance, which summarises the whole model rather than individual predictions.
- ✗
Partial dependence plots
Why it's wrong here
Partial dependence plots show the average marginal effect of a feature across all observations, describing global model behaviour rather than any single prediction. They are tempting because they visualise feature-target relationships clearly, but per-instance attribution requires SHAP values or SageMaker Clarify local explanations.
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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. An e-commerce company uses a recommendation system built with Amazon Personalize. They want to explain to customers why certain products are recommended. Which AWS service can provide model explanations?
medium- A.AWS Config
- B.AWS CloudTrail
- C.Amazon Detective
- ✓ D.Amazon SageMaker Clarify
Why D: Amazon SageMaker Clarify is the correct choice because it provides model explainability features, including feature importance and SHAP-based explanations, which can be used to interpret why Amazon Personalize recommends specific products. This aligns with the requirement to explain recommendations to customers, as SageMaker Clarify integrates with Personalize to generate human-readable explanations for model predictions.
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.