AI-102 Plan and manage an Azure AI solution Practice Question
You are responsible for an Azure AI multi-agent system built on Microsoft Foundry. The system experiences frequent timeout errors when agents call external APIs. You need to implement a resilient pattern. What should you do?
⚠ Common exam trap
A common mix-up: candidates confuse increasing timeouts with solving transient failures, but timeouts only mask the problem and do not provide resilience against intermittent API errors.
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
✓
Implement retry logic with exponential backoff in the agent tool definitions
Implementing retry logic with exponential backoff in agent tool definitions is a standard resilience pattern for transient failures when calling external APIs. This approach, often using the Retry-After header or a custom backoff strategy, reduces load on the API and prevents cascading timeouts in a multi-agent system built on Microsoft Foundry. It aligns with the recommended practices for building robust AI solutions that depend on external services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement retry logic with exponential backoff in the agent tool definitions
Why this is correct
Retry with exponential backoff handles transient failures effectively.
- ✗
Disable retry attempts to avoid duplicate requests
Why it's wrong here
Disabling retries increases failure rate without improving resilience.
- ✗
Increase the global timeout for all agents
Why it's wrong here
Increasing timeout may lead to resource exhaustion and does not handle transient failures.
- ✗
Switch to synchronous agent calls
Why it's wrong here
Synchronous calls still timeout if API is slow.
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