AI-102 Practice Question: Implement natural language processing solutions
A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?
⚠ Common exam trap
Many exam-takers confuse Language Detection with other text analytics features like Sentiment Analysis or Entity Recognition, assuming any 'analysis' feature can identify language, but only Language Detection is purpose-built for this task.
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
Language Detection is the correct feature because it is specifically designed to identify the language of text input, returning a language name and a confidence score. In a multi-language chatbot, this detection step is essential to route the query to the appropriate language-specific model or handler. Azure AI Language Service provides a dedicated pre-built capability for language detection, which can be called via the REST API or SDK.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis scores opinion polarity (positive, negative, neutral) within text; it returns no language identifier, so the chatbot cannot route by locale. It is tempting because it also consumes raw message text, and it would be correct when the requirement is gauging customer mood or escalation priority rather than detecting which language the message is written in.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns salient terms from text, not an ISO language code, so it cannot drive language-based routing. It is tempting because it also analyses raw message content, and it would be correct when the requirement is surfacing main topics for indexing, search or summarisation rather than identifying the message's language.
- ✓
Language Detection
Why this is correct
Language Detection returns the detected language name and ISO code for incoming text, letting the chatbot route each message to the correct language model before processing. It is the prerequisite step; translation and sentiment analysis operate only after the language is known.
- ✗
Entity Recognition
Why it's wrong here
Entity Recognition extracts predefined categories like names or dates from text, but it does not classify the language of the input. The chatbot needs language detection before routing, which requires the Language Detection API—a separate capability that analyses script and linguistic patterns. This option tempts because Entity Recognition also processes raw text, but its purpose is semantic labelling, not identifying the source language.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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