AIF-C01 Guidelines for Responsible AI Practice Question
A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?
⚠ Common exam trap
AWS often tests the misconception that the first step to address bias is to immediately retrain or adjust thresholds, rather than using a dedicated bias detection tool like SageMaker Clarify to first diagnose the nature and extent of the bias.
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
✓
Use Amazon SageMaker Clarify to detect bias in predictions
Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the prediction threshold for the affected group
Why it's wrong here
Raising the threshold only for the affected group masks the disparity without diagnosing its cause, and applying group-specific cut-offs creates unequal clinical decisions. It is tempting because threshold tuning is a legitimate calibration technique, and would be correct if the goal were a uniform precision-recall trade-off across all patients.
- ✓
Use Amazon SageMaker Clarify to detect bias in predictions
Why this is correct
Amazon SageMaker Clarify quantifies bias across demographic groups using metrics such as disparate impact and equal opportunity difference, directly identifying the age-group disparity described. Detecting and measuring the bias before mitigation satisfies the stem's requirement for a responsible first step, since remediation cannot be targeted without first confirming which groups are affected.
- ✗
Retrain the model with more data from the affected group
Why it's wrong here
Retraining is a remediation step, not the first responsible action; the disparity's source must be characterised before altering data or the model. It is tempting because additional representative data genuinely reduces bias, and would be correct once analysis confirms under-representation as the root cause.
- ✗
Immediately retire the model to prevent harm
Why it's wrong here
Retiring the model halts a clinical service outright, discarding any benefit for unaffected patients before the disparity is even measured. It is tempting because withdrawing a harmful system is defensible, and would be correct if the bias were confirmed severe and no mitigation or human oversight could contain it.
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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.