AI-102 Plan and manage an Azure AI solution Practice Question
You are designing a chatbot using Azure AI Language. The chatbot must understand user intents and also extract entities like dates and locations. Which feature combination should you use?
⚠ Common exam trap
Many exam-takers confuse entity linking (which maps to external knowledge bases) with entity extraction (which pulls values directly from the utterance), leading them to choose Option B despite it lacking intent recognition.
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
✓
Conversational Language Understanding (CLU) with entities
Conversational Language Understanding (CLU) is the correct Azure AI Language feature for building a chatbot that understands user intents and extracts entities like dates and locations. CLU is specifically designed for natural language understanding (NLU) tasks, providing prebuilt and custom entity extraction alongside intent recognition, which directly matches the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Conversational Language Understanding (CLU) with entities
Why this is correct
CLU handles both intents and entities for chatbots.
- ✗
Sentiment analysis and entity linking
Why it's wrong here
Not designed for intent recognition.
- ✗
Custom text classification and key phrase extraction
Why it's wrong here
Custom text classification for documents, not conversational intents.
- ✗
Orchestration Workflow and custom text classification
Why it's wrong here
Orchestration routes intents but doesn't extract entities; custom text classification doesn't handle intents.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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