A general text model is adapted to classify medical documents.
Use the terms precisely in the design document.
Which statement is correct?
Pre-training learns broad capabilities from large data, while fine-tuning adds task- or domain-specific training.
Detailed explanation
A model can learn general patterns first and then update parameters using task data.
A model can learn general patterns first and then update parameters using task data.
Changing a prompt is prompt engineering; fine-tuning is additional model training.
Changing a prompt is prompt engineering; fine-tuning is additional model training.
Pre-training is a prior training stage, not automatic per-request updating.
Pre-training is a prior training stage, not automatic per-request updating.
A customized model still requires quality, bias, and safety evaluation.
A customized model still requires quality, bias, and safety evaluation.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドのDomain 2.2で基盤モデルの学習・適応用語を確認する。Expected result
事前学習、微調整、プロンプト変更の違いを説明できる。Key points
- General capability
- Additional training
- Continued evaluation
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.