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AIF-C01 practice question: Choosing FMs and inference parameters (4 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.

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An insurance company's assistant answers questions about policy wording.

Compliance wants answers to be as consistent and focused as possible, with little creative variation between runs.

Which inference parameter change helps MOST?

Choose one answer.

  1. A. Raise the temperature so the model explores many more possible word choices
  2. B. Raise the maximum response length so each answer is more complete
  3. C. Lower the temperature so the model favors its most likely tokens
  4. D. Add a stop sequence so the model ends after each sentence
Show the answer and explanation

Answer: C

  • A. Wrong: Higher temperature flattens the probability distribution, so less likely tokens are chosen more often. That makes answers more varied, the opposite of what compliance wants.
  • B. Wrong: Maximum length sets how many tokens a reply can contain. It does not make the wording more consistent, and a longer limit does not focus the answer.
  • C. Right: Correct. Lower temperature makes the model more deterministic, so it selects higher-probability outputs. Responses become more focused and repeatable, which suits a policy assistant. Learn more: Inference request parameters (opens in a new tab)
  • D. Wrong: A stop sequence ends generation when a chosen string appears. It can shorten answers but does not reduce variation in their content.

Explanation

Temperature shapes the probability distribution the model samples from. A lower temperature makes the model favor higher-probability outputs and gives more deterministic responses. Length limits and stop sequences change how much text is returned, not how consistent it is.

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