Compare foundation-model specifications.
Do not confuse learned scale with the length of one request.
Choose two correct statements.
Parameters are learned internal weights, while a context limit constrains tokens processed in one inference request.
Detailed explanation
They represent learned values used during computation.
They represent learned values used during computation.
It is separate from the number of learned parameters.
It is separate from the number of learned parameters.
Scale does not automatically optimize price or speed.
Scale does not automatically optimize price or speed.
Training data volume and per-request context are different.
Training data volume and per-request context are different.
Try it yourself
An example you can run in a temporary verification environment.
Amazon Bedrock公式モデル仕様とAIF-C01 Domain 2.1・2.2を確認する。Expected result
モデル規模と入力長制約の違いを説明できる。Key points
- Parameters
- Context
- Specification
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.