AI-102 Implement generative AI solutions Practice Question
You are building a generative AI solution that uses Azure OpenAI function calling to let the model invoke backend APIs. During testing, the model sometimes invents parameter values that cause API errors. You need to make function invocation more reliable. What should you do?
⚠ Common exam trap
The trap here is believing that adding more API documentation or creative sampling improves argument accuracy.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Define clear function schemas with typed parameters, required fields, and descriptions, and validate arguments before executing the API call.
Reliable function calling depends on well-defined schemas and validation. Typed parameters, required fields, and descriptive names help the model map user intent to correct arguments, and validating before execution prevents invalid values from reaching the API. Higher temperature, oversized specifications, and parallel-call limits do not enforce argument correctness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define clear function schemas with typed parameters, required fields, and descriptions, and validate arguments before executing the API call.
Why this is correct
Function calling relies on the model producing arguments that match the declared JSON schema. Precise types, required fields, and descriptions guide the model, while server-side validation rejects invalid arguments before they reach the API. Together they reduce invented values and prevent downstream errors.
- ✗
Set tool_choice to auto and increase the temperature to encourage more creative argument generation.
Why it's wrong here
Tool_choice auto lets the model decide whether to call a function, and higher temperature increases randomness in token selection. That makes parameter values less predictable, not more accurate. Creative sampling works against reliable, schema-conformant arguments and can worsen the invented-value problem.
- ✗
Disable parallel tool calls and force the model to call only one function per turn.
Why it's wrong here
Limiting parallel calls controls how many functions execute at once, not whether the arguments are valid. The model can still invent values within a single call. This setting affects orchestration and latency rather than schema adherence, so it does not address the root cause of API errors.
- ✗
Include the full OpenAPI specification of every backend API in the system message.
Why it's wrong here
Pasting large specifications consumes context and can dilute the model's focus. Function calling expects concise, well-defined function schemas rather than entire API documents. Excess detail may confuse parameter mapping and does not by itself enforce types or required fields, so reliability is not guaranteed.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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