AWS Certified AI Practitioner AIF-C01 Practice Question
In Amazon Bedrock, inference pricing for large language models is based on the number of input and output tokens that the service processes. When cost is the primary concern, which configuration change will most directly reduce the amount you are billed for each request?
Shorten the prompt and lower the maximum allowed output length.
Raise the top-k value to widen the token selection pool.
Enable streaming responses instead of batch delivery.
Increase the temperature so the model samples less common tokens.
Amazon Bedrock bills inference on a pay-as-you-go basis that is proportional to the total number of tokens the model consumes-both in the prompt (input) and in the generated text (output). Reducing those token counts-by shortening the prompt and setting a lower maximum for the model's response-cuts the billed units and therefore lowers cost. Temperature, top-k, and streaming settings influence style, randomness, or delivery of the response, but they do not change how many tokens are processed or what you pay.
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What are tokens in the context of language models like those in Amazon Bedrock?
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How does shortening the prompt and lowering the maximum output length reduce costs?
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What do temperature and top-k settings control in a language model?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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