easyMultiple Choice
AIF-C01 Practice Question: A data scientist is using SageMaker Clarify to…
A data scientist is using SageMaker Clarify to analyze a binary classification model for gender bias. The dataset has 80% male and 20% female applicants. The model predicts positive outcomes for 60% of males and 30% of females. Which fairness metric would directly capture this disparity in prediction rates?
⚠ Common exam trap
AIF-C01 often tests the distinction between demographic parity (equal prediction rates) and equalized odds (equal error rates) — candidates who focus on 'bias' generically may pick equalized odds without noticing the question only provides prediction rates, not ground-truth labels.
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
✓
Demographic parity
Demographic parity (also called statistical parity) measures whether the probability of a positive prediction is equal across groups. Here, 60% of males versus 30% of females receive positive outcomes, a clear disparity in prediction rates, which is exactly what demographic parity captures. It compares selection rates directly without conditioning on actual labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Equalized odds
Why it's wrong here
Equalized odds compares true positive and false positive rates across groups, requiring ground-truth labels; the stem supplies only prediction rates, so it cannot be computed. It is tempting because equalized odds is the standard metric when both error rates matter, but disparate impact matches prediction-rate disparity.
- ✗
Disparate impact
Why it's wrong here
Disparate impact is the ratio of positive prediction rates (30%/60% = 0.5), which is a related metric but the question asks for a metric that directly captures the disparity in prediction rates.
- ✗
Accuracy difference
Why it's wrong here
Accuracy difference compares overall correctness between groups, which needs actual outcome labels; the stem gives only positive prediction rates, so accuracy cannot be derived. It is tempting because accuracy difference is a recognised SageMaker Clarify metric, but it measures classification error rather than selection-rate disparity.
- ✓
Demographic parity
Why this is correct
Demographic parity compares positive prediction rates across groups, so 60% male versus 30% female reveals a clear disparity. Other metrics such as equalised odds or disparate impact assess error rates or ratios, not the raw outcome-rate gap described.
Quick reference
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.