- A
NER identifies grammatical parts of speech like nouns and verbs
Why wrong: Parts of speech tagging identifies grammatical roles — NER specifically identifies named entities like people, organizations, and places.
- B
NER identifies and categorizes named entities like people, organizations, locations, and dates in text
NER extracts named entities: 'Bill Gates' (Person), 'Microsoft' (Organization), 'Seattle' (Location), '1975' (Date).
- C
NER generates new names for products based on brand guidelines
Why wrong: Name generation is a creative task — NER identifies and categorizes existing named entities in text.
- D
NER converts names into anonymous placeholders for privacy
Why wrong: Data anonymization replaces names with placeholders — NER identifies and extracts named entities from 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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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) and provide an example of its output?
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
NER identifies and categorizes named entities like people, organizations, locations, and dates in text
Named entity recognition (NER) is a natural language processing (NLP) capability that identifies and classifies key elements in text into predefined categories such as person names, organizations, locations, dates, and quantities. Option B correctly describes this function, and its output typically includes the extracted entity along with its category label, for example, {'entity': 'Microsoft', 'category': 'Organization'}.
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.
- ✗
NER identifies grammatical parts of speech like nouns and verbs
Why it's wrong here
Parts of speech tagging identifies grammatical roles — NER specifically identifies named entities like people, organizations, and places.
- ✓
NER identifies and categorizes named entities like people, organizations, locations, and dates in text
Why this is correct
NER extracts named entities: 'Bill Gates' (Person), 'Microsoft' (Organization), 'Seattle' (Location), '1975' (Date).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
NER generates new names for products based on brand guidelines
Why it's wrong here
Name generation is a creative task — NER identifies and categorizes existing named entities in text.
- ✗
NER converts names into anonymous placeholders for privacy
Why it's wrong here
Data anonymization replaces names with placeholders — NER identifies and extracts named entities from text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse NER with other NLP tasks like part-of-speech tagging (Option A) or assume it involves generating or anonymizing data (Options C and D), rather than recognizing that NER is purely about identifying and categorizing existing entities in text.
Detailed technical explanation
How to think about this question
Under the hood, NER models in Azure AI Language use transformer-based architectures (e.g., BERT) fine-tuned on large annotated corpora to predict entity boundaries and types at the token level. A subtle behavior is that NER can handle overlapping entities (e.g., 'New York Times' as both a location and an organization) depending on the model configuration, and it often outputs a confidence score for each prediction. In a real-world scenario, NER is critical for extracting structured data from unstructured legal documents, such as identifying parties, dates, and monetary amounts in contracts.
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.
- →
Describe features of Natural Language Processing workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of Natural Language Processing workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: NER identifies and categorizes named entities like people, organizations, locations, and dates in text — Named entity recognition (NER) is a natural language processing (NLP) capability that identifies and classifies key elements in text into predefined categories such as person names, organizations, locations, dates, and quantities. Option B correctly describes this function, and its output typically includes the extracted entity along with its category label, for example, {'entity': 'Microsoft', 'category': 'Organization'}.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.