Home / AWS Certified AI Practitioner (AIF-C01) / 1 Practice / AWS-AIF-433
There is little data and repeated user records.
Compare models reliably.
Choose two practices.
Hint 1: Focus on the requirement Keep correlated people, devices, and future records out of the same evaluation boundary, and report variation across folds.
Hint 2: Key terms Review cross-validation splits.
Cross-validationRepeated training and evaluation across several data partitions.
Group splitA partition that keeps records from one entity in a single fold.
FoldOne training or validation partition in a cross-validation procedure.
Temporal leakageFuture information entering a training or evaluation partition. × Incorrect Respect group and time independence, report mean and variation across folds, and keep a final test set separate.
Detailed explanation A Incorrect Tune repeatedly on the final test result
The test becomes contaminated.
B Correct Keep the same user or future rows from leaking across folds
Independence is preserved.
C Incorrect Randomly split records from one user across folds
User patterns leak.
D Correct Report mean and variation and retain a separate final test
Uncertainty is visible.
Check official documentation Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.2の交差検証、グループ分割、時系列評価を確認する。
Official references Official documentation 1 Official documentation 2 What to confirm データの関係性を保った分割で、評価結果のばらつきを説明できる。
Key points Cross-validation Group split Variation 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 Cross-validation Repeated training and evaluation across several data partitions.
Group split A partition that keeps records from one entity in a single fold.
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