Operational Efficiency and Optimization for GenAI Applications A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A dental group uses a model on Amazon Bedrock to read patient referral emails and extract the referral number, the appointment date and the insurance amount into a JSON record. A downstream scheduling system rejects any record that does not match the expected format.
The same email often produces slightly different values or wording across runs, so the extracted fields change between two runs of the job. Auditors want the same email to produce the same record each time it is processed.
The extraction must stay on the same model and keep the JSON format, and the output should be as repeatable as the model allows.
Which change meets these requirements?
Choose one answer.
AA. Set the temperature to 0 and leave top-p at its default, so the model picks the most likely tokens and the same email gives nearly the same record each run.
BB. Raise the temperature to 0.9, run each email three times, and keep the most common value for each field, so that the random variation is removed by voting across the runs.
CC. Set top-k to 500 and top-p to 1.0 so that the model considers a wider range of word choices, which gives the extraction more room to find the correct referral number.
DD. Remove the JSON format instruction, and add a stop sequence of a closing brace so that the model ends each response early and the record is shorter.
Show the answer and explanation
Answer: A
A. Right: A temperature of 0 makes sampling nearly greedy, which makes outputs highly repeatable for the same input. Leaving top-p at its default adds no extra randomness, so the model and the JSON format stay unchanged. Learn more: Inference request parameters (opens in a new tab)
B. Wrong: A high temperature increases the variation between runs, and voting across several runs triples the cost without guaranteeing a single repeatable result. It works against the goal of the same record each time. Learn more: Inference request parameters (opens in a new tab)
C. Wrong: A wider range of choices increases variation between runs, and simple fields such as dates do not need more candidate words. The option moves the output away from repeatable results rather than toward them. Learn more: Inference request parameters (opens in a new tab)
D. Wrong: Removing the format instruction makes the output less likely to match the scheduling system's schema, and an early stop sequence can cut the JSON before it is complete. The option breaks the format requirement. Learn more: Inference request parameters (opens in a new tab)
Explanation
A temperature of 0 makes the model's sampling nearly deterministic, so the same email tends to yield the same extracted record, and the JSON instruction is kept. Higher temperature, wider sampling and format-breaking stop sequences all increase variation or break the schema.
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