- A
Renaming database fields to follow a consistent naming convention
Why wrong: Database naming conventions are a development practice — NER is an NLP technique for extracting entities from text.
- B
Identifying and classifying real-world entities (people, organisations, locations) mentioned in text
NER extracts and categorises named entities from text — enabling structured information extraction from unstructured content.
- C
Recognising the named author of a document for copyright purposes
Why wrong: Author attribution is document metadata — NER identifies all entities mentioned in text, not just authors.
- D
Detecting when a user provides their name in a chatbot conversation
Why wrong: Chatbot name detection is one narrow use case — NER is a general capability for all entity types across any text.
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.
What is 'named entity recognition' (NER) in Azure AI Language?
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.
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.
- ✗
Renaming database fields to follow a consistent naming convention
Why it's wrong here
Database naming conventions are a development practice — NER is an NLP technique for extracting entities from text.
- ✓
Identifying and classifying real-world entities (people, organisations, locations) mentioned in text
Why this is correct
NER extracts and categorises named entities from text — enabling structured information extraction from unstructured content.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Recognising the named author of a document for copyright purposes
Why it's wrong here
Author attribution is document metadata — NER identifies all entities mentioned in text, not just authors.
- ✗
Detecting when a user provides their name in a chatbot conversation
Why it's wrong here
Chatbot name detection is one narrow use case — NER is a general capability for all entity types across any text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's NER uses transformer-based models (e.g., BERT variants) fine-tuned on large annotated corpora to predict entity spans and types. It supports both pre-defined categories (like Person, Location, Organization) and custom entities via the Custom NER feature, which allows training on domain-specific data. A subtle behavior is that NER can disambiguate entities based on context, e.g., 'Paris' as a location vs. a person's name, using surrounding tokens and part-of-speech tags.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: 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.
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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Last reviewed: Jun 11, 2026
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.
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