AIF-C01 Guidelines for Responsible AI Practice Question
A company uses Amazon SageMaker to build a binary classification model for loan approvals. After training, the data science team wants to evaluate the model for potential bias against a protected group. Which AWS service should they use to compute bias metrics?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the distinction between monitoring tools (Model Monitor, Debugger) and bias detection tools (Clarify), leading candidates to confuse operational monitoring with fairness evaluation.
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
✓
Amazon SageMaker Clarify
Amazon SageMaker Clarify is the correct service because it is specifically designed to detect bias in machine learning models and datasets. It provides built-in bias metrics (e.g., difference in positive proportion, disparate impact) for both pre-training and post-training evaluation, making it the appropriate tool for assessing potential bias against a protected group in a binary classification model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations in deployed endpoints, not pre-deployment bias metrics. It is tempting because it watches model behaviour statistically, and it would be correct for alerting when production input distributions shift away from the training baseline, rather than computing fairness measures.
- ✗
Amazon SageMaker Debugger
Why it's wrong here
Debugger captures training tensors and profiles resource usage, not bias metrics against protected groups. It is tempting because it inspects model internals during training, and it would be correct for diagnosing vanishing gradients, overfitting, or excessive GPU memory consumption rather than fairness evaluation.
- ✓
Amazon SageMaker Clarify
Why this is correct
SageMaker Clarify computes bias metrics such as disparate impact and demographic parity on trained models, detecting potential bias against protected groups. It integrates directly with SageMaker training and endpoints, satisfying the requirement to evaluate the loan model for bias.
- ✗
Amazon SageMaker Experiments
Why it's wrong here
Experiments tracks and compares training runs, hyperparameters, and metrics, but computes no bias or fairness statistics. It is tempting because it organises model evaluation data, and it would be the right choice for comparing accuracy across multiple training trials, not for measuring disparate impact against a protected group.
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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.