4.1Standard
Resampling reduced a recall gap between groups.
Check side effects before and after deployment.
Choose two appropriate validations.
× Incorrect
Compare multiple group and overall metrics before and after the intervention and consider legal requirements and appeal outcomes.
Detailed explanation
IncorrectStop evaluation after deployment
Data and usage can change fairness over time.
CorrectReview legal, policy, and human-appeal outcomes
Numbers alone may not capture real-world impact.
CorrectCompare group and overall metrics before and after
One improved metric can hide a new degradation.
IncorrectReport only the improved metric
Other harms may be hidden.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.1の公平性評価と責任あるAI運用を確認する。
Official references
What to confirm
改善指標だけでなく副作用、法令、異議申立てを含めて検証できる。
Key points
- Before/after
- Side effects
- Continuous review
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
Fairness intervention
A change to data, weights, thresholds, or process intended to reduce unequal impact.
Trade-off
An improvement in one objective accompanied by a possible cost or deterioration in another.
Question IDAWS-AIF-126
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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