4.2Standard
A review model looks fair in aggregate.
Check combinations of attributes.
Choose two evaluations.
× Incorrect
Compare errors, thresholds, and outcomes for intersectional groups while considering sample size, uncertainty, privacy, and stakeholder context.
Detailed explanation
IncorrectEqual aggregate averages prove every combination is fair
Aggregation can hide gaps.
CorrectCompare counts, errors, thresholds, and outcomes for combinations of attributes
Small-group disparities can appear.
CorrectConsider sample size, confidence intervals, privacy, and stakeholder interpretation
Uncertainty and context are covered.
IncorrectRemove small groups to stabilize statistics
Impact is hidden.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.2の公平性、バイアス、影響評価を確認する。
Official references
What to confirm
集計で隠れる交差グループの差を測定し、統計的な不確実性を説明できる。
Key points
- Intersectional group
- Fairness
- Confidence interval
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Intersectional group
A population defined by the combination of two or more attributes.
Aggregation bias
A misleading result caused by combining groups with different patterns.
Question IDAWS-AIF-428
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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