AWS Certified AI Practitioner AIF-C01 Practice Question
When invoking a foundation language model through Amazon Bedrock, the team lowers the temperature parameter from 1.0 to 0.1. How is the model's output most likely to change?
Responses become more deterministic and consistent, showing less randomness.
The probability of generating entirely new creative ideas increases.
Responses become significantly longer because fewer stop conditions are applied.
The model now processes fewer input tokens, reducing prompt cost.
Temperature controls the amount of randomness the model uses when selecting the next token. A lower temperature (closer to 0) narrows the probability distribution, so the model chooses higher-probability tokens more often. This results in answers that are more deterministic and consistent. Changing temperature does not directly affect response length, prompt size, or make the output more creative-higher temperatures (nearer to 1 or above) increase creativity and variability, not lower ones.
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What is the temperature parameter in language models?
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How does a lower temperature affect the probability distribution in a language model?
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Why does temperature not impact token count or response length in language models?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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