Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What is 'named entity recognition' (NER) in Azure AI Language?
⚠ Common exam trap
Candidates often confuse NER with other NLP tasks like sentiment analysis or key phrase extraction, or assume it only handles names, when in fact it classifies a wide range of entity types including dates, quantities, and URLs.
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
✓
Identifying and classifying real-world entities (people, organisations, locations) mentioned in text
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Renaming database fields to follow a consistent naming convention
Why it's wrong here
Renaming database fields to follow a naming convention is a schema-management and development-practice activity unrelated to natural language processing. NER works on unstructured text input, not on structured database schemas, and its 'entities' are mentions of real-world objects within sentences, not attributes or columns in a data model. This option is wrong because it misapplies the term 'entity' from data modelling to a text-analytics service.
- ✓
Identifying and classifying real-world entities (people, organisations, locations) mentioned in text
Why this is correct
Named entity recognition (NER) in Azure AI Language identifies and categorises real-world entities such as people, organisations, locations, dates, and quantities mentioned in unstructured text, returning each entity span with a type and confidence score. This is exactly the core function of NER: converting free-form text into structured data for downstream search, summarization, and analytics. It is correct because it accurately describes the prebuilt capability that Azure AI Language provides.
- ✗
Recognising the named author of a document for copyright purposes
Why it's wrong here
Recognising the named author of a document for copyright purposes confuses document-level metadata with text-level entity extraction. NER extracts named entities from the body of the text — so if the author's name appears in the prose, it might be tagged as a Person — but it cannot determine authorship, legal ownership, or copyright status, which rely on provenance, registration, and rights information outside the text. This option is wrong because it treats NER as a copyright-analysis tool rather than a content-entity extractor.
- ✗
Detecting when a user provides their name in a chatbot conversation
Why it's wrong here
This option describes a single conversational trigger — recognizing when a user types their own name — but NER is a broad text-analytics capability that scans any input and labels every recognized named entity with a type such as Person, Organisation, Location, or DateTime. Detecting 'when' a user provides their name requires additional intent and condition logic on top of NER; the service itself simply returns the name entity and its category. It is wrong because it reduces a general-purpose NLP capability to one narrow use case rather than defining what NER fundamentally does.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Named entity recognition
Named entity recognition (NER) is an Azure AI service feature that automatically identifies and classifies key pieces of information in text, such as names of people, organizations, locations, dates, and other specific data.
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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