AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?
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
✓
Configure a maximum iteration step limit in the agent orchestration loop
Implementing agent execution limits, such as maximum iteration counters or step limits within the orchestration loop, prevents agents from falling into infinite tool-calling loops.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the vector store chunk overlap size
Why it's wrong here
Chunk overlap affects document chunking for RAG ingestion, unrelated to agent reasoning loops.
- ✓
Configure a maximum iteration step limit in the agent orchestration loop
Why this is correct
Setting a max iteration threshold halts execution if the agent exceeds expected reasoning steps, preventing infinite loops.
- ✗
Enable Azure API Management caching for the tool endpoint
Why it's wrong here
Caching API responses would return identical data faster but would not stop the agent from repeatedly invoking the tool.
- ✗
Set the model frequency penalty to 2.0
Why it's wrong here
Frequency penalty discourages repetitive token generation in text output, but does not govern agent tool-calling loops.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-103 exam.