1.3Standard
A pretrained model is available for a small business dataset.
Compare reuse options.
Choose two descriptions.
× Incorrect
Transfer learning reuses learned representations, but target distribution, license, and performance must be checked before selective tuning.
Detailed explanation
IncorrectTarget data quality and license do not matter for transfer learning
Source constraints remain.
CorrectReuse pretrained representations when they fit the target task
Existing knowledge can reduce data and compute.
IncorrectSmall data always requires retraining every parameter
Compute and overfitting risks exist.
CorrectCheck target distribution, license, and performance before tuning selected layers
Source and use must fit.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.3の転移学習、ファインチューニング、モデル適合性を確認する。
Official references
What to confirm
学習済みモデルを再利用する利点と、対象データ・ライセンス確認の必要性を説明できる。
Key points
- Transfer learning
- Reuse
- License
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
Using knowledge learned for one task or domain to improve another task.
Fine-tuning
Updating some or all model parameters with task-specific examples.
Question IDAWS-AIF-417
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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