1.2Standard
Synthetic examples are added for a rare class.
Manage training risk.
Choose two checks.
× Incorrect
Compare distributions, boundaries, duplicates, leakage, protected attributes, and privacy with real data, and evaluate on a real validation set.
Detailed explanation
CorrectCompare distribution, boundary cases, duplicates, leakage, and protected attributes with real data
Quality and bias are tested.
Quality and bias are tested.
CorrectEvaluate performance, fairness, and privacy on a real-data validation set
Real-world generalization is measured.
Real-world generalization is measured.
IncorrectArtificial data is always anonymous and needs no review
Reidentification and bias can remain.
Reidentification and bias can remain.
IncorrectDuplicate synthetic rows in train and test without limits
Evaluation becomes optimistic.
Evaluation becomes optimistic.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2の合成データ、品質、公平性、プライバシーを確認する。Expected result
合成データを無条件に信頼せず、現実データで有効性とリスクを確認できる。Key points
- Synthetic data
- Reidentification
- Validation
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-453
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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