Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
Which Azure AI Language Feature Detects the Language of Text?
A multinational corporation receives customer support emails in multiple languages. They need to automatically identify the language of each email so it can be routed to the appropriate support team. Which Azure AI Language feature should they use?
Quick Answer
The answer is the Language Detection feature within Azure AI Language. This is correct because Language Detection is a prebuilt capability specifically designed to analyze text input and identify its dominant written language, returning both the language name and its ISO 639-1 code. For a multinational corporation routing multilingual support emails, this feature directly maps to the need for automatic language identification without requiring custom models. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of the core prebuilt features in Azure AI Language, often appearing alongside questions about sentiment analysis or key phrase extraction as a distractor. A common trap is confusing Language Detection with Translation, but remember: detection identifies the language, while translation converts it. A useful memory tip is to think of Language Detection as the “bouncer” that reads the text’s ID card before letting it into the correct support queue.
⚠ Common exam trap
A common mix-up: candidates confuse language detection with sentiment analysis or key phrase extraction because all three are Natural Language Processing features, but only language detection answers the 'which language?' question directly.
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 Azure AI Language feature because it is specifically designed to identify the written language of text input. The multinational corporation's requirement to automatically determine the language of each email for routing directly matches the core functionality of this prebuilt capability, which returns a language name and ISO 639-1 code for each document.
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 determines whether text is positive, negative, or neutral, but it does not identify the language.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important words and phrases in the text, but it does not detect the language.
- ✓
Language detection
Why this is correct
Language detection automatically identifies the language of the input text, making it the correct feature for this routing scenario.
- ✗
Entity recognition
Why it's wrong here
Entity recognition identifies and categorizes named entities like people, places, and organizations, but it does not detect the language.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Language detection
Language detection is an Azure AI service that automatically identifies the language of a piece of text without manual input.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A customer support team receives emails in multiple languages. They want to automatically determine the language of each email and then extract key phrases to summarize the issue. Which two Azure AI Language features should they use in sequence?
medium- A.Sentiment analysis and key phrase extraction
- B.Language detection and entity extraction
- ✓ C.Language detection and key phrase extraction
- D.Entity extraction and sentiment analysis
Why C: The scenario requires first identifying the language of each email (using Language Detection) to enable accurate processing of multilingual content. Then, to summarize the issue, Key Phrase Extraction directly extracts key phrases from the text. The stem explicitly asks for 'extract key phrases to summarize the issue,' so only Language Detection followed by Key Phrase Extraction (Option C) achieves this. Option B uses Entity Extraction, which identifies entities like names or dates but does not extract key phrases for summarization. Options A and D are incorrect because Sentiment Analysis is not used for summarization, and starting with Entity Extraction without language detection is less effective.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.