AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying a chatbot using Azure AI Bot Service and Language Understanding (LUIS). The bot must understand user intent from free-text input. Which component should you train?
⚠ Common exam trap
Watch out — candidates often confuse the role of LUIS with QnA Maker, assuming both handle any text input, but LUIS is for intent classification from free-text conversation, while QnA Maker is for retrieving answers from a fixed knowledge base, not for understanding dynamic user intents.
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
✓
Language Understanding (LUIS) model
The Language Understanding (LUIS) model is the correct component to train because the bot needs to interpret free-text user input and extract intent. LUIS is a natural language processing service specifically designed for intent recognition and entity extraction from conversational phrases. Training the LUIS model with labeled utterances teaches it to map user expressions to predefined intents, enabling the bot to understand and respond appropriately.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Language Understanding (LUIS) model
Why this is correct
LUIS is the component that maps free-text utterances to intents, so its model must be trained with example utterances and labelled intents. Azure AI Bot Service only orchestrates conversation; it performs no intent classification itself.
- ✗
Speech-to-text model
Why it's wrong here
A speech-to-text model converts audio into written words; it performs no intent classification, so free-text utterances would never map to intents. It is tempting because bots often accept voice input, and transcription is a genuine prerequisite for that channel — but the stem specifies typed free-text, where LUIS utterance training supplies the intent.
- ✗
QnA Maker knowledge base
Why it's wrong here
A QnA Maker knowledge base matches questions against curated answer pairs; it returns a stored response rather than classifying an utterance into an intent, so no intent model is trained. It is tempting because QnA Maker handles FAQ-style conversational answers, which suits a bot answering fixed questions, not one routing free-text by intent.
- ✗
Computer Vision model
Why it's wrong here
A Computer Vision model analyses images for objects, text and faces; it extracts no meaning from typed sentences, so intent recognition fails. It is tempting because bots can process uploaded images, and vision would be the right component for reading a photographed document or receipt — not for classifying free-text utterances.
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