AWS Certified AI Practitioner AIF-C01 Practice Question
A developer using a generative text model on Amazon Bedrock lowers the top-p (nucleus sampling) value from 0.9 to 0.2 while keeping all other inference parameters unchanged. What outcome should the developer expect from this change?
The responses will become longer because the model can use a larger context window.
The responses will be more focused and deterministic, showing less diversity in wording.
The responses will be more random and creative because the sampling pool expands.
The model will be more likely to switch languages within a single response.
Top-p limits the cumulative probability mass from which the next token is sampled. A lower value means the model selects among fewer, higher-probability tokens, so responses become more deterministic and less varied. It does not lengthen responses, add multilingual capability, or increase randomness; in fact, randomness and creativity decrease because fewer potential tokens are considered at each step.
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What is top-p (nucleus sampling) in generative models?
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How does lowering the top-p value make responses more deterministic?
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How is top-p different from temperature in generative models?
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What is nucleus sampling (top-p) in generative models?
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How does reducing the top-p value affect the output of a generative model?
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Why does a lower top-p value decrease randomness in generated text?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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