AI-102 Practice Question: Implement natural language processing solutions
A developer is building a multilingual chatbot using Azure AI Language. The bot must detect the user's language automatically and route the query to the appropriate language-specific model. Which Azure AI Language feature should the developer use?
⚠ Common exam trap
A common mix-up: candidates confuse the Translator API's built-in language detection capability with the dedicated Language Detection API, assuming the Translator API is sufficient, but the exam expects you to choose the feature whose primary purpose matches the requirement—pure language detection—rather than a multi-purpose tool.
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 API.
The Language Detection API is the correct choice because it is specifically designed to identify the language of input text automatically, returning a language code and confidence score. This enables the chatbot to route the query to the appropriate language-specific model without requiring any prior training or configuration. The other options either require explicit language specification or are designed for different tasks like translation or intent classification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Translator API.
Why it's wrong here
Translator translates text between languages; it does not identify the source language for routing within Azure AI Language. It is tempting because multilingual scenarios often involve translation, but the requirement is detection, which the Language Detection feature provides directly.
- ✗
Conversational language understanding (CLU) with multilingual project.
Why it's wrong here
CLU with a multilingual project extracts intents and entities across languages but does not return the detected language for routing. It is tempting because it handles multilingual utterances, yet the stem requires identifying the language first, which the Language Detection feature performs.
- ✓
Language detection API.
Why this is correct
The Language detection API returns the detected language and ISO code for input text, letting the bot identify the user's language before routing the query to the matching language-specific model. This satisfies the automatic detection requirement without manual selection.
- ✗
Custom text classification model.
Why it's wrong here
Custom text classification assigns labels to documents; it neither detects language nor routes queries. It is tempting because custom models handle domain-specific text, but language identification requires the dedicated Language Detection feature, which returns the detected language and confidence score for routing.
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