AWS Certified AI Practitioner AIF-C01 Practice Question
A developer increases the Top-K parameter from 5 to 50 when invoking a foundation model through Amazon Bedrock. Which immediate impact is most likely on the model's next-token selection?
The model will consider a larger set of high-probability tokens, increasing the diversity of possible responses.
The model will automatically reduce the temperature value, making responses more deterministic.
The model will shorten the maximum output length because fewer tokens remain available to select.
The model will switch to cumulative-probability filtering (Top-P), thereby decreasing randomness.
Top-K sampling limits the model to choosing from the K highest-probability tokens at each generation step. Raising K enlarges that candidate pool-from 5 to 50 in this case-so the model can draw from many more tokens. This usually produces more varied and creative outputs because the probability mass is spread across a wider set of possibilities. Changing Top-K does not directly shorten output length, alter temperature values, or switch the sampling method to cumulative-probability (Top-P) filtering.
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What is Top-K sampling in foundation models?
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How does increasing the Top-K value affect diversity in model outputs?
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What is the difference between Top-K sampling and Top-P (nucleus) sampling?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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