1.3Standard
Repeated training produces different scores.
Make selection reproducible.
Choose two practices.
× Incorrect
Record seeds, splits, environments, code, and settings, and evaluate several seeds for mean, variation, and safety rather than one lucky result.
Detailed explanation
CorrectRecord random seed, data split, environment, code, and model settings
Differences can be reproduced and compared.
Differences can be reproduced and compared.
CorrectEvaluate several seeds for average, variation, and safety
A single lucky result is avoided.
A single lucky result is avoided.
IncorrectChoose from the highest single score
Random variation is ignored.
Random variation is ignored.
IncorrectChange splits every time without saving settings
Comparisons are impossible.
Comparisons are impossible.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の実験管理、再現性、モデル評価を確認する。Expected result
確率的な学習結果を再現可能な条件で比較し、ばらつきも判断できる。Key points
- Seed
- Variation
- Reproducibility
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
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.
Question IDAWS-AIF-394
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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