2.1Standard
Adapt a model trained on general images to medical images.
Explain reuse and its limitations.
Choose two correct statements.
× Incorrect
Transfer learning reuses representations from another domain, but target-domain distribution, labels, and negative transfer must be evaluated.
Detailed explanation
IncorrectA high source score eliminates target evaluation
Domain differences still matter.
IncorrectTransfer learning needs no target labels or evaluation
The target task still requires validation.
CorrectEvaluate target-domain distribution and performance
The source model may not generalize or may cause negative transfer.
CorrectTransfer learning reuses representations learned on another task or domain
It can reduce the target-data requirement.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 2.1の基盤モデル、転移学習、適応を確認する。
Official references
What to confirm
転移学習の利点と、対象ドメイン評価の必要性を説明できる。
Key points
- Reuse
- Domain shift
- Negative transfer
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
Transfer learning
Reusing representations or weights learned for one task or domain in another task.
Negative transfer
A mismatch in transferred knowledge that reduces performance on the target task.
Question IDAWS-AIF-175
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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