The answer is Language Detection. This Azure AI Language feature is specifically designed to automatically identify the language of a given text input, returning both the language name and a confidence score, which makes it the perfect tool for routing multilingual product reviews to the correct translation queue. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between the core Azure AI Language features: Language Detection identifies the language, while Sentiment Analysis gauges emotion and Key Phrase Extraction pulls out important terms. A common trap is confusing Language Detection with the Translator service, but remember that detection is a prerequisite for translation, not the translation itself. For a quick memory tip, think of Language Detection as the “language bouncer” that checks the ID of your text before letting it into the translation club.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A global e-commerce company receives product reviews in multiple languages. They want to automatically identify the language of each review to route it to the appropriate translation queue. Which Azure AI Language feature should they use?
Why wrong: Sentiment Analysis detects the emotional tone (positive, negative, neutral) of text, not the language.
B
Key Phrase Extraction
Why wrong: Key Phrase Extraction returns a list of key phrases or main points from the text, but it does not identify the language.
C
Language Detection
Language Detection is designed to identify the language in which text is written, supporting over 100 languages. It is the correct feature for routing reviews based on language.
D
Entity Recognition
Why wrong: Entity Recognition extracts named entities such as people, places, and organizations from text, but it cannot determine the language of the text.
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 language of a given text input, returning a language name and a confidence score. This directly meets the requirement to automatically detect the language of product reviews so they can be routed to the appropriate translation queue.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 detects the emotional tone (positive, negative, neutral) of text, not the language.
✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns a list of key phrases or main points from the text, but it does not identify the language.
✓
Language Detection
Why this is correct
Language Detection is designed to identify the language in which text is written, supporting over 100 languages. It is the correct feature for routing reviews based on language.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity Recognition
Why it's wrong here
Entity Recognition extracts named entities such as people, places, and organizations from text, but it cannot determine the language of the text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might confuse Language Detection with Sentiment Analysis or Key Phrase Extraction because all three are Natural Language Processing features, but only Language Detection answers the specific question of identifying the language of the text.
Trap categories for this question
Keyword trap
Key Phrase Extraction returns a list of key phrases or main points from the text, but it does not identify the language.
Detailed technical explanation
How to think about this question
Language Detection in Azure AI Language uses a deep learning model trained on a large corpus of multilingual text to classify text into over 120 languages. It can handle mixed-language documents by returning the dominant language and can also detect scripts like Cyrillic or Devanagari. A subtle behavior is that very short text (e.g., a single word) may yield lower confidence scores, so for production routing, a confidence threshold (e.g., 0.8) is often applied to avoid misrouting.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Language Detection — Language Detection is the correct Azure AI Language feature because it is specifically designed to identify the language of a given text input, returning a language name and a confidence score. This directly meets the requirement to automatically detect the language of product reviews so they can be routed to the appropriate translation queue.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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 multinational company receives customer feedback in multiple languages. They need to automatically determine the language of each piece of feedback before routing it to the appropriate support team. Which prebuilt Azure AI Language feature should they use?
medium
A.Sentiment Analysis
✓ B.Language Detection
C.Key Phrase Extraction
D.Named Entity Recognition (NER)
Why B: The Language Detection feature in Azure AI Language is specifically designed to identify the language of a given text input, returning the language name and a confidence score. This directly matches the requirement to automatically determine the language of customer feedback before routing it to the appropriate support team.
Variation 2. A global company receives customer support tickets in over 60 languages. They need to automatically detect the language of each ticket so it can be routed to the appropriate language-specific team. The company has no labeled training data for language identification. Which Azure AI Language feature should they use?
medium
A.Custom Text Classification
✓ B.Language Detection
C.Key Phrase Extraction
D.Entity Recognition
Why B: Language Detection is the correct choice because it is a pre-built, zero-shot Azure AI Language feature that can automatically identify the language of text without requiring any labeled training data. The service uses a multilingual model trained on large datasets to detect over 100 languages, making it ideal for routing support tickets in over 60 languages with no prior customization.
Last reviewed: Jun 11, 2026
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