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.
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.
Which Azure AI service can identify and extract named entities (people, organizations, locations, dates) from text?
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.
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.
✗
Azure AI Vision
Why it's wrong here
Azure AI Vision analyzes images — named entity recognition operates on text.
✓
Azure AI Language (Named Entity Recognition)
Why this is correct
Azure AI Language's NER feature extracts and categorizes entities like people, organizations, and locations from text.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure AI Translator
Why it's wrong here
Azure AI Translator converts text between languages — NER is a feature of Azure AI Language.
✗
Azure AI Speech
Why it's wrong here
Azure AI Speech handles audio — named entity recognition is a text analytics feature.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's NER uses pre-trained deep learning models (e.g., Transformer-based architectures like BERT) fine-tuned on large annotated corpora to predict entity spans and types. The service supports both pre-built categories (e.g., Person, Organization, Location, DateTime) and custom entity extraction via custom NER. In a real-world scenario, a legal firm could use NER to automatically extract party names, dates, and court locations from thousands of contract documents, significantly reducing manual review time.
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: 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.
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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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.
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Question Discussion
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