Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are configuring an Azure OpenAI completion endpoint that will be exercised by an automated regression test. The test must verify the text of the model's reply byte-for-byte, so you need to eliminate as much randomness as possible during generation. Which parameter configuration should you set on the request to achieve the highest determinism?
temperature 1.0, top_p 0.95, frequency_penalty 0, presence_penalty 0
temperature 0.7, top_p 0.5, frequency_penalty 0, presence_penalty 0
temperature 0, top_p 1.0, frequency_penalty 0, presence_penalty 0
temperature 0, top_p 0.1, presence_penalty 1.0, frequency_penalty 0
Temperature controls how much randomness is applied when sampling tokens; a value of 0 turns randomness off. When temperature is 0, top_p is ignored, but leaving it at 1.0 does not re-introduce randomness. Frequency-penalty and presence-penalty bias the probability of tokens based on prior appearance, so setting them to 0 preserves determinism. Therefore, the configuration with temperature 0, top_p 1.0, and both penalties 0 produces the most repeatable output. Raising temperature, reducing top_p, or applying either penalty all introduce variability and can cause successive calls with the same prompt to differ.
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