Microsoft Azure AI Engineer Associate AI-102 Practice Question
When using Azure AI Foundry, you configure a GPT-4 deployment in Azure OpenAI to produce contract summaries. The summaries must be repeatable so that each time the same prompt is sent the model returns the same text. You are allowed to generate up to 300 tokens. Which parameter configuration should you apply to minimize randomness while still allowing the model to return the full summary?
Setting temperature to 0 switches the decoder to argmax sampling, eliminating stochastic variation. Leaving top_p at the default 1.0 means nucleus sampling does not discard any tokens, so the distribution is unchanged. Setting max_tokens to 300 only caps length and has no effect on determinism. Any non-zero temperature (0.2, 0.7, 1.0) keeps probabilistic sampling enabled, so results can differ across calls. Reducing top_p below 1 would also introduce variability because the candidate-token set can change between calls. Presence or frequency penalties by themselves are deterministic, but when combined with a non-zero temperature they still yield non-repeatable outputs. Using the optional seed parameter can further strengthen reproducibility, but temperature 0 with top_p 1 is the minimum configuration required.
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