Topic 1 Question 76
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost. Which solution will meet these requirements?
Customize the model by using fine-tuning.
Decrease the number of tokens in the prompt.
Increase the number of tokens in the prompt.
Use Provisioned Throughput.
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- 正解だと思う選択肢: B
Bedrock pricing is based on the number of tokens processed, which includes both input tokens (from the prompt) and output tokens (generated by the model). By decreasing the number of tokens in the prompt, you directly reduce the cost associated with each invocation of the model.
👍 6Blair772024/11/12 - 正解だと思う選択肢: D
D. Use Provisioned Throughput
To lower the monthly cost, the company can use Provisioned Throughput (PT) to scale their model's resource utilization. This allows them to pay only for the actual compute time used by the model, rather than paying a fixed monthly fee.
👍 1AzureDP9002025/01/25 - 正解だと思う選択肢: B
B. Decrease the number of tokens in the prompt: In a few-shot learning scenario, the number of tokens used in the prompt contributes directly to the cost, as you're billed based on the number of tokens processed during each invocation. By decreasing the number of tokens in the prompt, the company can reduce the cost per invocation while still maintaining the model's performance. This can be done by reducing the number of examples or making the examples more concise.
👍 1Jessiii2025/02/11
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