AI-102 Practice Question: Implement natural language processing solutions
You are building a multilingual chatbot using Azure AI Language. For a given user utterance, you need to first detect the language, then route to the appropriate language-specific intent model. Which combination of Azure AI Language features should you use?
⚠ Common exam trap
Many candidates confuse Translator (which changes the language) with Language Detection (which identifies the language without altering the text), leading them to choose Option C 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
✓
Language Detection and Conversational Language Understanding
The scenario requires first detecting the language of the user utterance (using Language Detection) and then routing to a language-specific intent model (using Conversational Language Understanding, which supports multiple languages in separate projects or deployments). This combination directly fulfills the requirement of language-aware intent routing.
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 Detection and Conversational Language Understanding
Why this is correct
Language Detection identifies the utterance's language, then Conversational Language Understanding applies the matching language-specific intent and entity model. This pairing satisfies the requirement to detect language first and route to the correct project for intent recognition.
- ✗
Key Phrase Extraction and Conversational Language Understanding
Why it's wrong here
Key Phrase Extraction surfaces salient terms; it cannot identify a language, so routing to a language-specific intent model never happens. It would suit mining topics from documents, whereas the stem requires Language Detection before Conversational Language Understanding.
- ✗
Translator and Conversational Language Understanding
Why it's wrong here
Translator converts text between languages; it does not return the detected source language for routing decisions. It would be correct when translating utterances into a single pivot language, but the stem needs Language Detection feeding Conversational Language Understanding.
- ✗
Language Detection and Custom Text Classification
Why it's wrong here
Custom Text Classification assigns documents to user-defined classes; it does not resolve intents from utterances. It would fit tagging support tickets by topic, whereas the stem requires Language Detection followed by Conversational Language Understanding for intent routing.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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