Practice Questions

---

Question 1

What is the primary purpose of entity recognition in Natural Language Processing?

A. Determine the sentiment of text
B. Identify important topics in a document
C. Extract specific real-world items from text
D. Translate text between languages

Correct Answer: C

Explanation:
Entity recognition focuses on identifying specific real-world entities such as people, organizations, locations, dates, and numbers—not sentiment, topics, or translations.

---

Question 2

Which Azure service provides entity recognition capabilities?

A. Azure Machine Learning
B. Azure AI Language
C. Azure AI Vision
D. Azure Cognitive Search

Correct Answer: B

Explanation:
Entity recognition is part of Azure AI Language, which provides NLP capabilities such as entity recognition, sentiment analysis, and key phrase extraction.

---

Question 3

A company wants to extract names, dates, and organization names from customer emails. Which NLP capability should be used?

A. Sentiment analysis
B. Language detection
C. Key phrase extraction
D. Entity recognition

Correct Answer: D

Explanation:
Entity recognition is designed to extract specific items like names, dates, and organizations from unstructured text.

---

Question 4

Which of the following is an example of an entity?

A. “Customer feedback”
B. “Very satisfied”
C. “Microsoft”
D. “Fast delivery”

Correct Answer: C

Explanation:
“Microsoft” is a named organization, which qualifies as an entity. The other options describe concepts or sentiments, not entities.

---

Question 5

What type of entity recognition is used to identify email addresses and phone numbers?

A. Key phrase extraction
B. Language detection
C. Personally Identifiable Information (PII) detection
D. Sentiment analysis

Correct Answer: C

Explanation:
PII detection is a form of entity recognition that identifies sensitive personal data, such as phone numbers and email addresses.

---

Question 6

Which scenario is the best use case for entity recognition?

A. Summarizing long documents
B. Translating text into different languages
C. Flagging sensitive data in support tickets
D. Measuring customer satisfaction

Correct Answer: C

Explanation:
Entity recognition (specifically PII detection) is commonly used to identify sensitive data for compliance and security purposes.

---

Question 7

Does entity recognition in Azure AI Language require you to train a custom machine learning model?

A. Yes, always
B. Yes, but only for large datasets
C. No, it uses pretrained models
D. Only if multiple languages are involved

Correct Answer: C

Explanation:
For AI-900, entity recognition uses prebuilt, pretrained models, requiring no ML expertise or training.

---

Question 8

Which feature distinguishes entity recognition from key phrase extraction?

A. Entity recognition identifies emotions
B. Entity recognition detects specific real-world items
C. Entity recognition summarizes text
D. Entity recognition translates languages

Correct Answer: B

Explanation:
Entity recognition identifies specific, concrete items (names, places, dates), while key phrase extraction identifies important topics or concepts.

---

Question 9

A document contains the sentence:

“The meeting is scheduled for March 10 in Seattle.”

Which entities could be identified?

A. Sentiment and key phrases
B. A date and a location
C. An organization and a person
D. A language and a topic

Correct Answer: B

Explanation:
“March 10” is a date, and “Seattle” is a location, both of which are standard entity types.

---

Question 10

Which statement about entity recognition is true?

A. It only works with structured data
B. It requires labeled training data
C. It identifies specific items within unstructured text
D. It is used only for translation tasks

Correct Answer: C

Explanation:
Entity recognition analyzes unstructured text to identify specific, meaningful items, such as names, places, and dates.

---

Final Exam Takeaways

• Entity recognition answers “Who, what, where, and when?”
• It is provided by Azure AI Language
• No model training is required
• Commonly tested alongside key phrase extraction and sentiment analysis
• PII detection is a key real-world use case

---

Go to the AI-900 Exam Prep Hub main page.

Where this fits in the exam

• Exam domain: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
• Sub-area: Identify features of common NLP workload scenarios
• Key skill tested: Understanding what entity recognition is, what it’s used for, and which Azure service provides it

You are not expected to build or train models—only to recognize capabilities and use cases.

---

What Is Entity Recognition?

Entity recognition (also called Named Entity Recognition or NER) is an NLP capability that identifies and categorizes specific, real-world items mentioned in text.

These items (entities) typically fall into predefined categories such as:

• People
• Organizations
• Locations
• Dates and times
• Numbers
• Products
• Email addresses, phone numbers, URLs

Simple example

Input text:

“Satya Nadella is the CEO of Microsoft, headquartered in Redmond.”

Extracted entities:

• Person: Satya Nadella
• Organization: Microsoft
• Location: Redmond

---

Azure Service That Provides Entity Recognition

Entity recognition is provided by Azure AI Language, part of Azure’s AI services portfolio.

Key points for the exam:

• Uses prebuilt models
• No machine learning expertise required
• Accessed via REST APIs or SDKs
• Supports multiple languages

---

Types of Entity Recognition in Azure AI Language

For AI-900, you mainly need to understand the concept, but it helps to know the types at a high level.

1. Named Entity Recognition

Identifies common entity categories, such as:

• Person
• Location
• Organization
• Date
• Quantity

2. Personally Identifiable Information (PII) Detection

Detects sensitive personal data, including:

• Phone numbers
• Email addresses
• Social security numbers
• Credit card numbers

This is often tested conceptually in the context of compliance and data privacy.

---

Common Use Cases for Entity Recognition

1. Information Extraction

Automatically pull important data from unstructured text such as:

• Contracts
• Emails
• Reports
• Support tickets

2. Search and Indexing

Improve search by identifying names, locations, or products mentioned in documents.

3. Data Classification and Tagging

Label documents based on recognized entities to:

• Route support tickets
• Organize content
• Trigger workflows

4. Compliance and Security

Detect and flag PII to:

• Prevent data leaks
• Meet regulatory requirements
• Mask sensitive data

---

Entity Recognition vs Other NLP Capabilities

This comparison is very exam-relevant.

Capability	What it identifies
Entity recognition	Specific items (names, places, dates)
Key phrase extraction	Main topics and concepts
Sentiment analysis	Emotional tone
Language detection	Language of the text

If the question asks “Who, where, or what specifically?” → entity recognition
If it asks “What is this text about?” → key phrase extraction

---

Key Features to Remember for the Exam

• Uses pretrained models
• Works with unstructured text
• Supports multiple languages
• Does not require labeled training data
• Commonly used for information extraction and compliance

---

Responsible AI Considerations

Microsoft emphasizes responsible AI even at the fundamentals level.

Important considerations:

• Entity recognition may misidentify entities due to ambiguity
• Results should be reviewed before being used for critical decisions
• Sensitive data detection should align with privacy and compliance policies

---

Exam Tips

• Expect scenario-based questions, not code
• Focus on matching the right NLP capability to the scenario
• Look for keywords like:
  • names, addresses, dates, organizations → Entity recognition
  • topics, summaries → Key phrase extraction
  • opinions, feelings → Sentiment analysis

---

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.