AI-102 Plan and manage an Azure AI solution Practice Question
A developer is configuring an Azure AI Language resource for sentiment analysis. The solution must process social media posts in real-time with a throughput of 1000 requests per minute. After testing, the developer notices that the API returns a 429 (Too Many Requests) error when the load exceeds 500 requests per minute. What is the most likely cause and solution?
⚠ Common exam trap
Many candidates confuse rate limiting with transient errors and choose retry logic (C), not realizing that a consistent 429 at a specific threshold indicates a hard capacity cap that only a tier upgrade can resolve.
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
✓
Upgrade the Azure AI Language resource to a higher tier (e.g., Standard S) to increase the rate limit.
The 429 error indicates the request rate exceeds the resource's allocated tier limit. Azure AI Language resources have predefined rate limits per pricing tier; the Standard S tier offers higher throughput (e.g., 1,000 requests per minute) compared to lower tiers. Upgrading to Standard S directly increases the rate limit to match the required 1,000 requests per minute, making it the correct solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale out the resource by creating multiple Azure AI Language instances and load balancing requests.
Why it's wrong here
Rate limits are per resource; multiple instances would increase total throughput but not the limit per instance.
- ✓
Upgrade the Azure AI Language resource to a higher tier (e.g., Standard S) to increase the rate limit.
Why this is correct
The Free tier has a limit of 20 requests per minute; upgrading to Standard S allows up to 1000 requests per minute.
- ✗
Implement retry logic with exponential backoff to handle 429 errors.
Why it's wrong here
This is a workaround but does not address the root cause of insufficient capacity.
- ✗
Use Azure API Management to cache responses and reduce calls.
Why it's wrong here
Caching may not help with real-time processing and does not resolve the rate limit issue.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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