AI-102 Plan and manage an Azure AI solution Practice Question
Your team is developing a chatbot using Azure AI Bot Service with language understanding via Azure AI Language (CLU). You need to ensure that the chatbot can handle multiple intents in a single user utterance and return confidence scores for each. Which configuration should you use?
⚠ Common exam trap
Watch out — candidates often confuse Orchestration workflow (which routes to different projects) with the ability to handle multiple intents within a single CLU project, leading them to choose Option B incorrectly.
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 Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled.
Azure AI Language's Conversational Language Understanding (CLU) supports a 'Multiple intents' setting that allows the model to predict multiple intents from a single utterance, each with its own confidence score. This is the native way to handle multi-intent scenarios without requiring orchestration or custom logic.
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 Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled.
Why this is correct
CLU supports multiple intents per utterance with confidence scores.
- ✗
Use an Orchestration workflow project that connects to multiple CLU projects.
Why it's wrong here
Orchestration routes to different projects but does not return multiple intents from a single utterance.
- ✗
Use a standard CLU project and manually combine intents.
Why it's wrong here
Standard CLU returns a single predicted intent per utterance.
- ✗
Use Azure AI QnA Maker with custom logic to detect intents.
Why it's wrong here
QnA Maker does not support intent detection.
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