AI-102 Implement an agentic solution Practice Question
You are implementing an agentic solution using Azure AI Agent Service. The agent needs to maintain conversation context across multiple turns. You configure the agent with a custom prompt that includes a 'system message' and 'few-shot examples'. However, after a few turns, the agent starts repeating the same responses. What is the most likely cause?
⚠ Common exam trap
Test-takers frequently confuse 'max_tokens' (response length) with context window limits, or assume that repetition is caused by high temperature, when in fact it is the loss of conversation history due to context truncation that leads to repetitive outputs.
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
✓
The 'max_context_length' is set too low, causing earlier turns to be truncated
When the 'max_context_length' is set too low, the agent's conversation history is truncated after a few turns, removing earlier user inputs and assistant responses. This loss of context causes the model to lose track of the conversation flow and revert to repeating responses, as it no longer has the full context to generate varied replies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The 'max_tokens' parameter is set too low for the response
Why it's wrong here
This limits response length, not repetition.
- ✓
The 'max_context_length' is set too low, causing earlier turns to be truncated
Why this is correct
Truncation loses context, leading to repetition.
- ✗
The agent is using a 'context recycling' feature that resets after each turn
Why it's wrong here
Context recycling is not default and would not cause repetition.
- ✗
The temperature is set too high, causing the model to become deterministic
Why it's wrong here
High temperature increases randomness, not determinism.
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