4.1Standard
A review model shows reasons for its decision.
The explanation must match what the model actually supports.
Choose two practices.
× Incorrect
Fit the explanation method to the audience and model, validate its usefulness, and state that it may not establish causal or complete reasoning.
Detailed explanation
CorrectState that an explanation may be an estimate rather than a complete causal reason
Overconfidence is reduced.
IncorrectAn explanation removes the need for fairness or data-quality checks
Other controls remain necessary.
IncorrectNatural language means the reason is the true model cause
Post-hoc explanations can differ from computation.
CorrectValidate feature contributions or examples for the audience and model type
The explanation method is fit for purpose.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.1の説明可能性、透明性、モデル限界を確認する。
Official references
What to confirm
説明の有用性と限界を分け、誤解を招く説明を避けられる。
Key points
- Explainability
- Causality
- Limit
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
Explainability
The extent to which people can understand factors related to a model output.
Post-hoc explanation
A reason generated after prediction that may not faithfully represent internal computation.
Question IDAWS-AIF-344
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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