Build an internal app for Japanese summarization and structured extraction.
Compare language support, context length, and output format.
Choose two selection criteria.
Evaluate representative Japanese inputs and confirm that the model's context and structured-output capabilities meet the requirement.
Detailed explanation
Real business inputs test language quality better than a name or generic benchmark alone.
A name does not prove language quality or format support.
The largest model may not meet cost or latency requirements.
Context limits and structured-output support affect suitability.
Check official documentation
Compare the following points with the official documentation.
Amazon Bedrock公式モデル情報とAIF-C01 Domain 3.1のモデル選択観点を確認する。
Official references
What to confirm
実データによる品質確認とモデル仕様の要件照合を説明できる。
Key points
- Requirement fit
- Representative data
- Model specifications
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
Requirement evidence
A model name or benchmark score does not establish quality for a specific language and task.
Constraint matching
Input capacity and output structure are functional requirements, not cosmetic details.