AI-102 Practice Question: Implement natural language processing solutions
You are building a multilingual support chatbot using Azure AI Language. The chatbot must understand user queries in English, Spanish, and French, and respond in the same language. The solution should minimize latency and cost. What is the recommended approach?
⚠ Common exam trap
A common mix-up: candidates assume translation is necessary for multilingual support, overlooking Azure AI Language's built-in multilingual capability, which is more efficient and cost-effective.
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 Azure AI Language project with multilingual support enabled.
Azure AI Language supports multilingual projects natively, allowing a single model to process queries in English, Spanish, and French without additional translation steps. This minimizes latency (no round-trip translation) and cost (no Translator API consumption), while maintaining response accuracy in the user's original language.
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 Azure AI Translator to translate all queries to English, process with an English-only project, then translate responses back.
Why it's wrong here
Translator adds latency and cost, and may lose context.
- ✓
Use a single Azure AI Language project with multilingual support enabled.
Why this is correct
Multilingual support handles multiple languages in a single project, reducing latency and cost.
- ✗
Create separate Azure AI Language projects for each language and route queries based on detected language.
Why it's wrong here
Multiple projects increase management overhead and cost.
- ✗
Use a single English-only project and rely on Azure AI Translator for all non-English queries.
Why it's wrong here
English-only with translation adds latency and may lose context.
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