AIF-C01 practice question: Choosing FMs and inference parameters (6 of 7)
Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A tax advisory firm's assistant sends the same 40-page tax guide, followed by a short customer question, to a model on Amazon Bedrock thousands of times a day.
Responses are slow and input costs are high. The guide changes only once a year.
Which feature addresses BOTH problems with the least change?
Choose one answer.
AA. Prompt caching, with the unchanging tax guide placed first in the prompt
BB. A higher temperature, so the model processes the guide more quickly
CC. Continued pre-training on the guide, repeated for every customer question
DD. A larger maximum response length, so fewer separate requests are needed
Show the answer and explanation
Answer: A
A. Right: Correct. Prompt caching reuses the processed prefix of a prompt, so the unchanging guide does not need to be processed again. That lowers latency and input cost, provided the guide comes before the variable question. Learn more: Prompt caching for faster model inference (opens in a new tab)
B. Wrong: Temperature controls randomness in the output. It does not change how fast the model reads input or how many input tokens are billed.
C. Wrong: Customization runs as a training job, which would be slow and costly if repeated for every question. It is not a per-request feature.
D. Wrong: A longer response limit only permits longer replies. It does not reduce the size of the guide sent with each request, and it adds output tokens.
Explanation
Prompt caching on Amazon Bedrock stores a contiguous prefix of the prompt, such as a long document or system prompt, so repeated requests reuse it. Cached tokens are billed at a lower read rate and reduce latency. The cache needs a stable prefix, so the static guide must come before the customer question. Cache entries expire after a time-to-live, five minutes by default for most models, which resets on each hit.
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