AI-102 Practice Question: Implement natural language processing solutions
You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?
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 a single CLU multilingual project that supports all three languages.
Azure AI Language's CLU (Conversational Language Understanding) supports multilingual projects, allowing you to train a single model that handles English, Spanish, and French. This minimizes development effort by avoiding separate projects for each language. Option A is wrong because translating all utterances to English adds latency and complexity, and may lose nuances. Option B is wrong because creating separate CLU projects for each language increases maintenance and deployment overhead. Option D is wrong because LUIS does not offer the same multilingual capabilities as CLU and is being deprecated.
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 the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.
Why it's wrong here
Adds translation cost and latency.
- ✗
Create a separate CLU project for each language and combine them with a routing mechanism.
Why it's wrong here
Separate projects increase development and maintenance effort.
- ✓
Use a single CLU multilingual project that supports all three languages.
Why this is correct
Multilingual project leverages cross-lingual transfer and reduces effort.
- ✗
Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.
Why it's wrong here
LUIS does not support multilingual projects as robustly as CLU.
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