Home / AWS Certified AI Practitioner (AIF-C01) / 3 Practice / AWS-AIF-358
Humans grade generated summaries.
Reduce evaluator variation.
Choose two practices.
Hint 1: Focus on the requirement Write the criteria and examples first, blind reviewers to model identity when possible, and preserve disagreement for calibration.
Hint 2: Key terms Review evaluation rubrics and blinded human review.
RubricA table of evaluation criteria, levels, and examples used for consistent judgments.
Blinded evaluationReviewing outputs without seeing model or condition labels that could bias judgment.
Inter-rater agreementThe degree to which independent reviewers make the same judgment.
Calibration sessionA review exercise used to align evaluators on criteria and examples. × Incorrect Use a rubric for accuracy, completeness, evidence, readability, and safety, with training, examples, blind review, multiple graders, and adjudication.
Detailed explanation A Correct Use a rubric defining accuracy, completeness, evidence, readability, and safety
Evaluation criteria are aligned.
B Incorrect Let every evaluator use different criteria
Results cannot be compared.
C Incorrect Save only the mean and not disagreements
Improvement evidence is lost.
D Correct Provide training, examples, blind review, multiple graders, and adjudication
Subjective variation can be measured and reduced.
Check official documentation Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 3.4の人手評価、品質基準、生成AI評価を確認する。
Official references Official documentation 1 Official documentation 2 What to confirm 評価者間で一貫した品質評価を行い、不一致も改善へ利用できる。
Key points Rubric Blind review Agreement 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 Rubric A table of evaluation criteria, levels, and examples used for consistent judgments.
Blinded evaluation Reviewing outputs without seeing model or condition labels that could bias judgment.
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