AI-103 Certification Practice Question #35

Question

You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Accepted Answer

The goal is to improve output stability so small wording differences stop breaking the validation system, while still maximizing reasoning quality. Output stability is controlled by sampling temperature: MS Learn states lower temperature makes output more focused and deterministic, so the lowest value, 0, produces the most consistent wording across runs (temperature 1 is the random default and 2 is even more random). Reasoning quality is controlled by the reasoning effort: the `effort` parameter accepts low/medium/high, and reducing it trades away reasoning, so "high" maximizes reasoning quality as required. Together, temperature 0 + effort "high" satisfies both stated objectives. Confidence is high-but-not-maximal because there is no Suggested Answer or community vote in the file to corroborate; the selections are inferred directly from the documented semantics of each parameter.

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