AWS Certified AI Practitioner AIF-C01 Practice Question
When generating a text response with an Amazon Bedrock foundation model, a developer raises the top-p setting from 0.4 to 0.9. How will this change most likely affect the model's token selection?
The model will consider a broader set of probable tokens, increasing response diversity.
The model will restrict choices to only the single highest-probability token, making output deterministic.
The model will automatically shorten the maximum length of the generated response.
The model will repeat tokens from the prompt more frequently to preserve context.
Top-p (nucleus) sampling instructs the model to choose from the smallest pool of candidate tokens whose cumulative probability meets the specified p value. A higher top-p includes a larger slice of the probability distribution, allowing the model to sample from more possible next tokens. This generally leads to more diverse and creative text. Lowering the value restricts the pool, making output more deterministic. Top-p does not directly control maximum response length and does not force token repetition.
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What is top-p sampling in text generation?
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Why is increasing the top-p value associated with more diverse responses?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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