Adapt a model to an internal writing style.
Manage data quality and confidentiality.
Choose two appropriate preparations.
Record purpose, provenance, labels, and quality criteria, and verify permission, anonymization, and access control for adaptation data.
Detailed explanation
This supports reproducibility and explains what the adaptation changes.
This supports reproducibility and explains what the adaptation changes.
Fine-tuning data is subject to privacy and authorization requirements.
Fine-tuning data is subject to privacy and authorization requirements.
Poor data can reduce quality and increase privacy risk.
Poor data can reduce quality and increase privacy risk.
Adaptation can change quality and safety and needs reevaluation.
Adaptation can change quality and safety and needs reevaluation.
Try it yourself
An example you can run in a temporary verification environment.
Amazon Bedrock公式モデルカスタマイズとAIF-C01 Domain 3.1の訓練データ管理を確認する。Expected result
微調整データの品質・権限・再評価の必要性を説明できる。Key points
- Provenance
- Confidentiality
- Reevaluation
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
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.