AI-102 Practice Question: Implement natural language processing solutions
A company is building a chatbot using Azure AI Language. The chatbot must detect user intent from utterances and also extract key entities like dates and product names. The solution must minimize latency for real-time conversation. Which approach should the team use?
⚠ Common exam trap
Candidates often confuse the legacy LUIS service (Option B) with the current CLU feature, or incorrectly assume that splitting the workload into two calls (Option C) could be faster, when in fact the single-call joint model is the optimized path for low-latency real-time intent and entity extraction.
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 Conversational Language Understanding (CLU) feature of Azure AI Language, which supports both intent and entity extraction in a single API call.
The Conversational Language Understanding (CLU) feature of Azure AI Language is specifically designed to handle both intent recognition and entity extraction in a single API call, which minimizes latency for real-time conversations. CLU is the modern replacement for LUIS and is optimized for conversational scenarios, supporting orchestration and prebuilt entities like dates and product names.
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 QnA Maker with a custom question-answer pair for each intent and entity.
Why it's wrong here
QnA Maker is not designed for intent detection.
- ✗
Use the Language Understanding (LUIS) service with a single call for both intent and entity extraction.
Why it's wrong here
LUIS is being deprecated and does not have the same performance optimizations.
- ✗
Use two separate calls to the Azure AI Language API: one for intent recognition and one for entity extraction.
Why it's wrong here
Separate calls increase latency.
- ✓
Use the Conversational Language Understanding (CLU) feature of Azure AI Language, which supports both intent and entity extraction in a single API call.
Why this is correct
CLU combines both tasks, minimizing latency.
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