What is Named Entity Recognition (NER) in Azure AI Language?
Which Azure AI service can identify and extract named entities (people, organizations, locations, dates) from text?
Quick Answer
The correct answer is Azure AI Language, specifically its Named Entity Recognition (NER) capability, because this service is purpose-built to identify and extract categories like people, organizations, locations, and dates from unstructured text. NER functions as a core component of Azure’s Natural Language Processing (NLP) workload, analyzing text to label entities with predefined types. On the AI-900 exam, this question tests your understanding of which Azure service handles entity extraction versus other tasks like sentiment analysis or translation—a common trap is confusing Azure AI Language with Azure AI Translator or Azure Bot Service. Remember that NER is about “who, what, where, and when” in text, so if you see a scenario asking for extracting names, places, or dates, think Azure AI Language. A helpful memory tip: NER stands for “Name Entity Recognition,” and the “N” in NER can remind you of “Names” as the primary output.
⚠ Common exam trap
Many candidates confuse Azure AI Language's NER with Azure AI Vision's OCR (Optical Character Recognition), mistakenly thinking that 'extracting entities from text' includes extracting text from images, but NER specifically operates on already-digitized text, not images.
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
✓
Azure AI Language (Named Entity Recognition)
Azure AI Language's Named Entity Recognition (NER) capability is specifically designed to identify and categorize named entities such as people, organizations, locations, and dates from unstructured text. This is a core feature of the Natural Language Processing (NLP) workload within Azure AI Language, making option B the correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Vision
Why it's wrong here
Azure AI Vision processes images and video, returning captions, object labels or OCR text, so it cannot parse a sentence into entity categories. It is tempting because OCR extracts text from pictures, but that output is raw characters, not classified people, organisations, locations or dates. Azure AI Language performs the named-entity extraction this scenario requires.
- ✓
Azure AI Language (Named Entity Recognition)
Why this is correct
Azure AI Language's Named Entity Recognition feature extracts people, organisations, locations and dates from unstructured text, satisfying the stem's requirement to identify named entities. It is purpose-built for this task, unlike vision or speech services, and returns categorised entity spans directly from submitted text.
- ✗
Azure AI Translator
Why it's wrong here
Azure AI Translator performs language translation between text, returning translated strings rather than entity annotations. It is the right service when the requirement is converting content across languages, whereas entity extraction belongs to Azure AI Language's named entity recognition capability.
- ✗
Azure AI Speech
Why it's wrong here
Azure AI Speech handles speech-to-text, text-to-speech, translation and speaker recognition; it does not perform named entity recognition on written text. It is tempting because it is a genuine Azure AI cognitive service processing language content, but entity extraction belongs to Azure AI Language, which returns people, organisations, locations and dates from text input.
Go deeper
Related to this question
Learn chapter
Conversational Language Understanding (CLU)
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Key term
NLP
NLP (Natural Language Processing) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in a way that is meaningful and useful.
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Same concept, more angles
1 more way this is tested on AI-900
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Variation 1. What is 'named entity recognition' (NER) in Azure AI Language?
easy- A.Renaming database fields to follow a consistent naming convention
- ✓ B.Identifying and classifying real-world entities (people, organisations, locations) mentioned in text
- C.Recognising the named author of a document for copyright purposes
- D.Detecting when a user provides their name in a chatbot conversation
Why B: Named entity recognition (NER) is a feature of Azure AI Language that identifies and categorizes real-world entities such as people, organizations, locations, dates, and quantities within unstructured text. It uses pre-trained machine learning models to extract these entities, enabling downstream tasks like information retrieval and content summarization. Option B correctly describes this core functionality.
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.