AI-102 Implement an agentic solution Practice Question
You are building an agentic solution using Microsoft Semantic Kernel. The agent uses a planner to orchestrate multiple functions. You want to improve the planner's ability to handle complex user requests that involve multiple steps. Which THREE strategies should you implement?
⚠ Common exam trap
Microsoft often tests the misconception that reducing function count (Option A) or using simpler prompts (Option D) improves planning, when in fact these strategies limit the planner's expressiveness and ability to handle complex, multi-step requests.
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
✓
Enable the planner to ask the user for clarification when the request is ambiguous
Enabling the planner to ask for clarification when a request is ambiguous allows the agent to resolve underspecified intents or missing parameters, which is critical for complex multi-step workflows. In Semantic Kernel, the planner can be configured with a 'user interaction' step that prompts for additional context, improving plan accuracy and reducing the risk of incorrect function chaining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Limit the number of available functions to reduce planning overhead
Why it's wrong here
Limiting functions reduces capability.
- ✓
Enable the planner to ask the user for clarification when the request is ambiguous
Why this is correct
Clarification improves accuracy.
- ✓
Create composite functions that encapsulate common multi-step sub-tasks
Why this is correct
Composite functions simplify planning.
- ✗
Use a simple, generic prompt to avoid overfitting
Why it's wrong here
Simple prompt may not handle complexity.
- ✓
Provide few-shot examples of multi-step workflows in the planner prompt
Why this is correct
Few-shot examples guide the planner.
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