AWS Certified AI Practitioner AIF-C01 Practice Question
A developer using an Amazon Bedrock text model needs deterministic, reproducible answers for nightly batch jobs. Which approach to the temperature parameter will minimize randomness in the generated text?
Set the temperature as close to 0.0 as possible.
Increase the maximum output length parameter.
Set the temperature to 1.0 or higher.
Leave temperature unchanged and only disable top-p sampling.
Temperature controls how random the next-token selection is during text generation. Values near 0 bias the model toward the single most-likely token at each step, producing predictable and repeatable output. Higher temperatures (for example, 1.0 or above) increase randomness. Disabling other sampling controls or adjusting maximum length does not directly reduce randomness like lowering temperature.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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