2.3Standard
A creative writer and a fixed classifier use the same model.
Adjust output variation.
Choose two descriptions.
× Incorrect
Temperature changes sampling variation at generation time; test lower values for stable extraction and higher values for appropriate creativity.
Detailed explanation
IncorrectHigher temperature guarantees factual answers
Creativity and factuality are separate.
CorrectTry lower temperature for fixed classification or extraction and evaluate accuracy
Unneeded randomness is reduced.
CorrectHigher temperature can increase variation for creative output
Sampling is broader.
IncorrectTemperature updates training data
It is a generation-time setting.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 2.3のtemperature、サンプリング、生成制御を確認する。
Official references
What to confirm
生成の多様性と定型タスクの安定性をtemperatureの観点で比較できる。
Key points
- Temperature
- Sampling
- Variation
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
Temperature
A generation parameter that changes how strongly likely tokens are favored.
Sampling
Selecting output tokens from a probability distribution rather than always taking the top token.
Question IDAWS-AIF-418
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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