1.2Basic
Income and age are inputs to a distance-based model.
Prevent one unit from dominating.
Choose two preprocessing practices.
× Incorrect
Fit scaling statistics on training data only and apply the same transform to validation and production while checking outliers and performance.
Detailed explanation
CorrectCompute mean and scale from training data and apply the same transform elsewhere
Evaluation leakage is avoided.
Evaluation leakage is avoided.
CorrectCheck outliers, distributions, and model performance after scaling
Side effects are measured.
Side effects are measured.
IncorrectCompute statistics on all data before splitting
Test information leaks.
Test information leaks.
IncorrectDifferent units never affect distance models
Scale can dominate distance.
Scale can dominate distance.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2の特徴量前処理、標準化、データ漏洩を確認する。Expected result
前処理の統計量を訓練データから分離して管理できる。Key points
- Standardization
- Leakage
- Outlier
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-434
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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