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 automatically reduce the temperature value, making responses more deterministic.
The model will consider a larger set of high-probability tokens, increasing the diversity of possible responses.
The model will switch to cumulative-probability filtering (Top-P), thereby decreasing randomness.
The model will shorten the maximum output length because fewer tokens remain available to select.
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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