AIF-C01 Guidelines for Responsible AI Practice Question
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?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Debugger's ability to monitor training metrics with model interpretability, but Debugger does not compute feature importance or explain predictions.
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
✓
SageMaker Clarify
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift, model quality degradation and bias in production endpoints; it does not compute feature importance. It is tempting because it analyses model behaviour, but clinicians need local and global feature attributions, which SageMaker Clarify supplies. Model Monitor would be correct for alerting on drift in a deployed endpoint.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments tracks, organises and compares training runs, metrics and artefacts; it computes no feature attributions. It is tempting because it analyses model training data, but interpretability for clinicians requires SageMaker Clarify's SHAP-based local and global feature importance. Experiments would be correct for comparing hyperparameter tuning job results.
- ✓
SageMaker Clarify
Why this is correct
SageMaker Clarify generates SHAP values that quantify each feature's contribution to individual predictions (local explanations) and aggregates them across the dataset for global feature importance, directly satisfying the clinicians' interpretability requirement. It also detects bias in training data and models, supporting responsible AI governance in medical contexts.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger captures tensors, gradients and training anomalies for debugging, not feature attributions. It is tempting because it inspects model internals, but clinicians need local and global feature importance, which SageMaker Clarify provides through SHAP-based explanations. Debugger would be correct for diagnosing vanishing gradients or poor convergence during training.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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