A compliance audit needs a hosted language model to return identical text whenever the same prompt and model version are replayed, so that archived answers can be reproduced exactly on demand. The team controls only the sampling settings. Which two configuration changes each make the output deterministic? Select TWO.
- ARaise the temperature toward 1.0 so the model weighs a broader spread of candidate tokens at each step.
- BSet the temperature to 0 so the model selects the single highest-probability token at every step. Correct
- CSet top-p to a value just above 0 so the nucleus collapses to only the single most-probable token. Correct
- DRaise top-p toward 1.0 so the model can draw from the entire probability distribution each step.
- EIncrease the maximum output-token limit so each archived answer has room to finish in full.
Why A is wrong: A temperature near 1.0 flattens the distribution and increases run-to-run variation, which is the opposite of the reproducibility the audit requires.
Why B is correct: Temperature 0 forces greedy decoding, picking the most-probable token each step, so a fixed prompt and model version yield the same text every time.
Why C is correct: A top-p that low keeps only the top token in the candidate set, leaving no alternatives to sample, which produces the same deterministic continuation each run.
Why D is wrong: A top-p near 1.0 leaves the full distribution available for sampling, so repeated calls can still diverge rather than reproducing identical text.
Why E is wrong: The output-token limit governs how long a response may run, not whether the same prompt reproduces the same wording, so determinism is unaffected.