AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying a custom question-answering solution using Azure AI Language. The solution must support multiple languages and provide answers from a set of FAQ documents. You need to ensure that the service can handle up to 1000 requests per minute with low latency. What should you configure?
⚠ Common exam trap
A common mix-up: candidates confuse throughput units (requests per minute vs. per second) and assuming that Azure AI Search replicas or provisioned TPS from other services apply to Azure AI Language custom question-answering, when the correct scaling mechanism is capacity units (CUs) on the Standard tier.
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 the Standard tier and allocate 10 capacity units (CUs).
Azure AI Language's custom question-answering feature uses a Standard tier with capacity units (CUs) to handle throughput and latency. Each CU provides a baseline of 100 requests per minute (RPM), so 10 CUs deliver exactly 1000 RPM, meeting the requirement. The Standard tier also supports multiple languages and low-latency responses from FAQ documents.
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 Azure AI Search with a basic tier and replicas.
Why it's wrong here
This is for search, not question answering.
- ✓
Use the Standard tier and allocate 10 capacity units (CUs).
Why this is correct
Standard tier with CUs provides the required throughput.
- ✗
Use the Free tier and enable autoscaling.
Why it's wrong here
Free tier is limited to 10 requests per minute.
- ✗
Deploy a dedicated endpoint with provisioned throughput of 1000 TPS.
Why it's wrong here
Custom question answering uses capacity units, not provisioned throughput.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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