Measuring intersectional fairness | AWS Certified AI Practitioner (AIF-C01) original practice question

4.2Standard

A review model looks fair in aggregate.

Check combinations of attributes.

Choose two evaluations.

Hint 1: Focus on the requirement

Identify what must change and what must remain unchanged. Then choose the command whose scope matches that requirement.

Hint 2: Check each command's role

Separate commands that display information, change state, remove data, or affect a child process.

Detailed explanation

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.

IncorrectEqual aggregate averages prove every combination is fair

Aggregation can hide gaps.

IncorrectRemove small groups to stabilize statistics

Impact is hidden.

An example you can run in a temporary verification environment.

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。

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。

Foundation review

Check whether the command acts on the current shell, a new process, an existing process, or a file.

Use the supplied command and compare the output with the expected result.

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