Home / AWS Certified AI Practitioner (AIF-C01) / 3 Practice / AWS-AIF-184
Display an AI answer to a customer.
Do not make a raw score look like certainty.
Choose two appropriate designs.
Hint 1: Focus on the requirement Show evidence and limits rather than presenting an uncalibrated internal score as a probability of correctness.
Hint 2: Key terms Review uncertainty communication for generated answers.
Uncertainty displayInformation about evidence strength, limitations, confidence, or verification shown to a user.
Uncalibrated scoreA model value that has not been shown to correspond reliably to an outcome probability.
Verification pathA documented way to inspect evidence, retry retrieval, or request human review.
OverconfidencePresenting a weakly supported result with more certainty than the evidence justifies. × Incorrect Display explainable evidence signals and avoid calling an uncalibrated internal score a probability; provide verification and human-review paths.
Detailed explanation A Correct Show evidence availability, retrieval match, and evaluation conditions
Users gain information about limitations.
B Incorrect Hide uncertainty to emphasize automation
Users cannot judge risk.
C Incorrect Display the internal score as exact correctness probability
Its meaning and calibration may be unknown.
D Correct Avoid claiming a raw score is a probability and provide review or search options
Unsupported certainty is reduced.
Check official documentation Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 3.3・4.2の生成AI評価、透明性、不確実性を確認する。
Official references Official documentation 1 Official documentation 2 What to confirm スコアと確率の違い、利用者向けの確認導線を説明できる。
Key points Uncertainty Evidence Overtrust 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 Uncertainty display Information about evidence strength, limitations, confidence, or verification shown to a user.
Uncalibrated score A model value that has not been shown to correspond reliably to an outcome probability.
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