AI-102 Plan and manage an Azure AI solution Practice Question
You are building a chatbot using Azure AI Bot Service and Language Service. The bot must recognize user intent for 'check order status'. How should you configure the Language Service?
⚠ Common exam trap
Watch out — candidates often confuse intent recognition with entity extraction or QnA, assuming any Language Service feature can handle intents, but only custom intent classification (or conversational language understanding) is designed for this purpose.
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
✓
Create a custom intent classification project
To recognize user intent for 'check order status', you need a custom intent classification project in Azure Language Service. This project type uses a trained model to map utterances to specific intents, such as 'CheckOrderStatus', which is exactly what the chatbot requires. Prebuilt models or QnA Maker do not provide custom intent recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a custom intent classification project
Why this is correct
A custom intent classification project in Azure AI Language trains a model on your labelled utterances, returning the top intent for 'check order status'. This satisfies the stem's requirement to recognise user intent, since conversational language understanding maps utterances to intents rather than extracting entities or answering questions.
- ✗
Deploy a QnA Maker knowledge base
Why it's wrong here
A QnA Maker knowledge base matches questions to stored answer pairs; it returns the closest answer rather than an intent label, so intent routing cannot be driven from it. It is correct when the bot must answer FAQs from a curated question-and-answer set.
- ✗
Configure a sentiment analysis endpoint
Why it's wrong here
Sentiment analysis scores opinion polarity in text, returning positive, negative or neutral values; it cannot map utterances to named intents. Intent recognition requires a Conversational Language Understanding project with trained intents and utterances. Sentiment analysis would be the right choice for routing angry customers or flagging negative feedback, not for detecting 'check order status'.
- ✗
Use the prebuilt entity extraction model
Why it's wrong here
Prebuilt entity extraction identifies items such as dates, numbers and emails within utterances; it returns entities, not intents, so 'check order status' would never be classified. It is correct when the bot must pull structured values from user text rather than determine what the user wants.
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