AI-102 Implement agentic AI solutions Practice Question
A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?
⚠ Common exam trap
Many candidates assume deterministic output (temperature=0) or reducing complexity (fewer functions) will fix reliability, when the real issue is semantic ambiguity in function definitions that the model cannot resolve without better descriptions.
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
✓
Provide better function descriptions with examples of when to use each function.
Providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on a dataset of correct function calls.
Why it's wrong here
Fine-tuning alters model weights for style or domain adaptation, not the schema-bound function selection the model performs at inference. It cannot prevent invented names, since function definitions are supplied per request. Fine-tuning suits teaching consistent output formatting or domain vocabulary when prompt engineering has plateaued, but here the fix is tightening function descriptions and parameters.
- ✗
Reduce the number of functions to only the most common ones.
Why it's wrong here
Removing functions reduces coverage of required tasks like CRM updates rather than improving selection accuracy; the model still lacks clear descriptions distinguishing remaining functions. Trimming is right when the schema exceeds token limits or contains genuinely unused definitions.
- ✗
Set the temperature parameter to 0 for deterministic output.
Why it's wrong here
Temperature controls sampling randomness, not schema adherence; at 0 the model still invents names when prompts are ambiguous. It is tempting because determinism sounds like reliability, and low temperature suits tasks needing reproducible phrasing, but function-name accuracy depends on clear descriptions and strict schema enforcement.
- ✓
Provide better function descriptions with examples of when to use each function.
Why this is correct
Function-calling accuracy depends on the model's understanding of each function's purpose. Richer descriptions stating when to invoke each function, plus concrete examples, reduce ambiguity and stop the model inventing or mis-selecting names, directly improving reliability.
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