1.1Standard
Several models produce predictions.
Evaluate combining them.
Choose two descriptions.
× Incorrect
An ensemble can reduce error or variance, but latency, cost, explainability, correlated errors, and versioning must be evaluated.
Detailed explanation
CorrectMeasure latency, cost, explainability, and performance against a single model
Complexity costs are known.
IncorrectIndividual model data and versions need not be tracked
Reproduction and audit are lost.
IncorrectMore models always improve accuracy
Correlated errors and overfitting exist.
CorrectCombining several predictions can reduce error or variance
Weaknesses may average out.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.1・1.3のアンサンブル、モデル比較、推論コストを確認する。
Official references
What to confirm
複数モデル化の利点と運用コスト・再現性の課題を説明できる。
Key points
- Ensemble
- Variance
- Tradeoff
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
Ensemble
A prediction made by combining outputs from multiple models.
Error correlation
The extent to which models make mistakes on the same examples.
Question IDAWS-AIF-456
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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