AI-102 Implement an agentic solution Practice Question
You are designing an agentic solution using Azure AI Agent Service with a custom skill that calls an external REST API. The API has rate limits: 100 requests per minute per client. You need to ensure the agent respects this limit without degrading user experience. Which approach should you take?
⚠ Common exam trap
Watch out — candidates often confuse reactive error-handling strategies (like handling 429 responses or exponential backoff) with proactive rate limiting, failing to recognize that only a distributed, preemptive mechanism like a token bucket with a shared counter can prevent rate limit violations without degrading user experience.
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
✓
Use a token bucket rate limiter with a shared counter stored in Azure Cache for Redis
A token bucket rate limiter with a shared counter stored in Azure Cache for Redis provides a distributed, atomic mechanism to enforce a strict 100 requests per minute per client limit across multiple instances of the agent. This approach ensures that the rate limit is respected globally without introducing unnecessary delays when the limit is not reached, thus maintaining a responsive user experience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a token bucket rate limiter with a shared counter stored in Azure Cache for Redis
Why this is correct
Token bucket allows burst and respects limits globally.
- ✗
Set a fixed delay of 600ms between each API call
Why it's wrong here
Fixed delay may underutilize or exceed limits.
- ✗
Configure the skill to handle HTTP 429 responses with retry-after logic
Why it's wrong here
Handling 429s degrades UX due to delays.
- ✗
Implement exponential backoff in the skill code
Why it's wrong here
Backoff can cause long pauses.
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