Category: Natural Language Processing (NLP)

AI, AI-900, Artificial Intelligence (AI), Large Language Models (LLMs), Machine Learning (ML), Microsoft Certification, Natural Language Processing (NLP)

Exam Prep Hub for AI-900: Microsoft Azure AI Fundamentals

Welcome to the one-stop hub with information for preparing for the AI-900: Microsoft Azure AI Fundamentals certification exam. The content for this exam helps you to “Demonstrate fundamental AI concepts related to the development of software and services of Microsoft Azure to create AI solutions”. Upon successful completion of the exam, you earn the Microsoft Certified: Azure AI Fundamentals certification.

This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the AI-900 exam and making use of as many of the resources available as possible.

Audience profile (from Microsoft’s site)

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.
This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:
- Basic cloud concepts
- Client-server applications
You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Skills measured at a glance (as specified in the official study guide)

  • Describe Artificial Intelligence workloads and considerations (15–20%)
  • Describe fundamental principles of machine learning on Azure (15–20%)
  • Describe features of computer vision workloads on Azure (15–20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
  • Describe features of generative AI workloads on Azure (20–25%)
Click on each hyperlinked topic below to go to the preparation content and practice questions for that topic. Also, there are 2 practice exams provided below.

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

Identify guiding principles for responsible AI

Describe fundamental principles of machine learning on Azure (15-20%)

Identify common machine learning techniques

Describe core machine learning concepts

Describe Azure Machine Learning capabilities

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

Identify Azure tools and services for computer vision tasks

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

Identify Azure tools and services for NLP workloads

Describe features of generative AI workloads on Azure (20–25%)

Identify features of generative AI solutions

Identify generative AI services and capabilities in Microsoft Azure

AI-900 Practice Exams

We have provided 2 practice exams (with answer keys) to help you prepare:

AI-900 Practice Exam 1 (60 questions with answers)

AI-900 Practice Exam 2 (60 questions with answers)

Important AI-900 Resources

To Do’s:

  • Schedule time to learn, study, perform labs, and do practice exams and questions
  • Schedule the exam based on when you think you will be ready; scheduling the exam gives you a target and drives you to keep working on it; but keep in mind that it can be rescheduled based on the rules of the provider.
  • Use the various resources above to learn and prepare.
  • Take the free Microsoft Learn practice test, any other available practice tests, and do the practice questions in each section and the two practice tests available on this exam prep hub.

Good luck to you passing the AI-900: Microsoft Azure AI Fundamentals certification exam and earning the Microsoft Certified: Azure AI Fundamentals certification!

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Practice Questions: Identify Natural Language Processing Workloads (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to automatically determine whether customer reviews are positive, negative, or neutral.

Which AI workload is required?

A. Text classification
B. Sentiment analysis
C. Language translation
D. Speech recognition

Correct Answer: B

Explanation: Sentiment analysis evaluates the emotional tone of text, such as opinions expressed in customer reviews.

Question 2

An organization needs to route incoming support emails to the correct department based on their content.

Which NLP capability best fits this scenario?

A. Key phrase extraction
B. Text summarization
C. Text classification
D. Language detection

Correct Answer: C

Explanation: Text classification assigns predefined labels or categories to text, making it ideal for routing emails by topic.

Question 3

A legal team wants to quickly identify names of people, organizations, and locations within long contracts.

Which NLP capability should be used?

A. Sentiment analysis
B. Named entity recognition
C. Text translation
D. Optical character recognition

Correct Answer: B

Explanation: Named entity recognition (NER) extracts structured entities such as people, organizations, and locations from unstructured text.

Question 4

A global company wants to translate product descriptions from English into multiple languages while preserving meaning.

Which AI workload is most appropriate?

A. Language detection
B. Text summarization
C. Language translation
D. Speech synthesis

Correct Answer: C

Explanation: Language translation converts text from one language to another while maintaining its original intent and meaning.

Question 5

An application needs to identify the main topics discussed in thousands of customer feedback messages.

Which NLP capability should be used?

A. Sentiment analysis
B. Key phrase extraction
C. Text classification
D. Question answering

Correct Answer: B

Explanation: Key phrase extraction highlights the most important concepts and terms within text.

Question 6

A chatbot answers common customer questions using a natural conversational interface.

Which AI workload does this represent?

A. Computer vision
B. Conversational AI / NLP
C. Speech AI only
D. Anomaly detection

Correct Answer: B

Explanation: Conversational AI uses NLP to understand user intent and generate natural language responses.

Question 7

A system must determine the language of incoming customer messages before processing them further.

Which NLP capability is required?

A. Text classification
B. Language detection
C. Named entity recognition
D. Text summarization

Correct Answer: B

Explanation: Language detection identifies the language used in a text sample.

Question 8

Which input type most strongly indicates a natural language processing workload?

A. Video streams
B. Audio recordings
C. Images and photos
D. Text documents

Correct Answer: D

Explanation: NLP workloads are centered on understanding and generating text-based data.

Question 9

A manager wants a short summary of long meeting transcripts to quickly understand key points.

Which NLP capability should be used?

A. Text summarization
B. Sentiment analysis
C. Language detection
D. Text classification

Correct Answer: A

Explanation: Text summarization condenses long text into a shorter, meaningful summary.

Question 10

An organization wants to ensure responsible use of AI when analyzing employee emails.

Which consideration is most relevant for NLP workloads?

A. Image resolution
B. Model latency
C. Data privacy and bias
D. Bounding box accuracy

Correct Answer: C

Explanation: NLP systems can introduce bias and raise privacy concerns when processing personal or sensitive text data.

Final Exam Tip

If a scenario focuses on understanding, classifying, translating, summarizing, or responding to text, it is almost always a natural language processing workload.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Identify Natural Language Processing Workloads (AI-900 Exam Prep)

Overview

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand, interpret, and generate human language. For the AI-900: Microsoft Azure AI Fundamentals exam, the goal is not to build language models, but to recognize NLP workloads, understand what problems they solve, and identify when NLP is the correct AI approach.

This topic appears under:

  • Describe Artificial Intelligence workloads and considerations (15–20%)
    • Identify features of common AI workloads

Most exam questions will be scenario-based, asking you to choose the correct AI workload based on how text is used.

What Is a Natural Language Processing Workload?

A natural language processing workload involves analyzing or generating language in written or spoken form (after speech has been converted to text).

NLP workloads typically:

  • Process unstructured text
  • Extract meaning, sentiment, or intent
  • Translate between languages
  • Generate human-like text responses

Common inputs:

  • Emails, chat messages, documents
  • Social media posts
  • Customer reviews
  • Transcribed speech

Common outputs:

  • Sentiment scores
  • Extracted keywords or entities
  • Translated text
  • Generated responses or summaries

Common Natural Language Processing Use Cases

On the AI-900 exam, NLP workloads are presented through everyday business scenarios. The following are the most important ones to recognize.

Text Classification

What it does: Categorizes text into predefined labels.

Example scenarios:

  • Classifying emails as spam or not spam
  • Routing support tickets by topic
  • Detecting abusive or inappropriate content

Key idea: The system assigns one or more labels to a piece of text.

Sentiment Analysis

What it does: Determines the emotional tone of text.

Example scenarios:

  • Analyzing customer reviews to see if feedback is positive or negative
  • Monitoring social media reactions to a product launch

Key idea: Sentiment analysis focuses on opinion and emotion, not topic.

Key Phrase Extraction

What it does: Identifies the main concepts discussed in a document.

Example scenarios:

  • Summarizing customer feedback
  • Highlighting important terms in legal or technical documents

Key idea: Key phrases help quickly understand what a document is about.

Named Entity Recognition (NER)

What it does: Identifies and categorizes entities in text.

Common entity types:

  • People
  • Organizations
  • Locations
  • Dates and numbers

Example scenarios:

  • Extracting company names from contracts
  • Identifying people and places in news articles

Language Detection

What it does: Identifies the language used in a text sample.

Example scenarios:

  • Detecting the language of customer messages before translation
  • Routing requests to region-specific support teams

Language Translation

What it does: Converts text from one language to another.

Example scenarios:

  • Translating product descriptions for global audiences
  • Providing multilingual customer support

Key idea: This workload focuses on preserving meaning, not word-for-word translation.

Question Answering and Conversational AI

What it does: Understands user questions and generates relevant responses.

Example scenarios:

  • Customer support chatbots
  • FAQ systems
  • Virtual assistants

Key idea: The system interprets intent and responds in natural language.

Text Summarization

What it does: Condenses long documents into shorter summaries.

Example scenarios:

  • Summarizing reports or meeting notes
  • Highlighting key points from articles

Azure Services Commonly Associated with NLP

For AI-900, you should recognize these services at a conceptual level.

Azure AI Language

Supports:

  • Sentiment analysis
  • Text classification
  • Key phrase extraction
  • Named entity recognition
  • Language detection
  • Summarization

This is the primary service referenced for NLP workloads on the exam.

Azure AI Translator

Supports:

  • Text translation between languages

Used specifically when scenarios mention multilingual translation.

Azure AI Bot Service

Supports:

  • Conversational AI solutions

Often appears alongside NLP services when building chatbots.

How NLP Differs from Other AI Workloads

Distinguishing NLP from other workloads is a common exam requirement.

AI Workload TypePrimary Input
Natural Language ProcessingText
Speech AIAudio
Computer VisionImages and video
Anomaly DetectionNumerical or time-series data

Exam tip: If the data is text-based and the goal is to understand meaning, sentiment, or intent, it is an NLP workload.

Responsible AI Considerations

NLP systems can introduce risks if not used responsibly.

Key considerations include:

  • Bias in language models
  • Offensive or harmful content generation
  • Data privacy when analyzing personal communications

AI-900 tests awareness, not mitigation techniques.

Exam Tips for Identifying NLP Workloads

  • Look for keywords like text, email, message, document, review, chat
  • Identify the goal: classify, analyze sentiment, extract meaning, translate, or respond
  • Ignore implementation details—focus on what problem is being solved
  • Choose the simplest AI workload that meets the scenario

Summary

For the AI-900 exam, you should be able to:

  • Recognize when a scenario represents a natural language processing workload
  • Identify common NLP use cases and capabilities
  • Associate NLP scenarios with Azure AI Language and related services
  • Distinguish NLP from speech, vision, and other AI workloads

A solid understanding of NLP workloads will significantly improve your confidence across multiple exam questions.

Go to the Practice Exam Questions for this topic.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Microsoft Certification, Natural Language Processing (NLP)

Identify Features of the Transformer Architecture (AI-900 Exam Prep)

Where This Topic Fits in the Exam

  • Exam domain: Describe fundamental principles of machine learning on Azure (15–20%)
  • Sub-area: Identify common machine learning techniques
  • Focus: Understanding what Transformers are, why they matter, and what problems they solve — not how to code them

The AI-900 exam tests conceptual understanding, so you should recognize key features, benefits, and common use cases of the Transformer architecture.

What Is the Transformer Architecture?

The Transformer architecture is a type of deep learning model designed primarily for natural language processing (NLP) tasks.
It was introduced in the paper “Attention Is All You Need” and has since become the foundation for modern AI models such as:

  • Large Language Models (LLMs)
  • Chatbots
  • Translation systems
  • Text summarization tools

Unlike earlier sequence models, Transformers do not process data sequentially. Instead, they analyze entire sequences at once, which makes them faster and more scalable.

Key Features of the Transformer Architecture

1. Attention Mechanism (Self-Attention)

The core feature of a Transformer is self-attention.

Self-attention allows the model to:

  • Evaluate the importance of each word relative to every other word in a sentence
  • Understand context and relationships, even when words are far apart

Example:
In the sentence “The animal didn’t cross the road because it was tired”, self-attention helps the model understand what “it” refers to.

📌 Exam takeaway: Transformers use attention to understand context more effectively than older models.

2. Parallel Processing

Traditional models like RNNs process text one word at a time.
Transformers process all words in parallel.

Benefits:

  • Faster training
  • Better performance on large datasets
  • Improved scalability in cloud environments (like Azure)

📌 Exam takeaway: Transformers are efficient and scalable because they don’t rely on sequential processing.

3. Encoder–Decoder Structure

Many Transformer-based models use an encoder–decoder architecture:

  • Encoder:
    • Reads and understands the input (e.g., a sentence in English)
  • Decoder:
    • Generates the output (e.g., the translated sentence in Spanish)

📌 Exam takeaway: Transformers often use encoders to understand input and decoders to generate output.

4. Positional Encoding

Because Transformers process words in parallel, they need a way to understand word order.

Positional encoding:

  • Adds information about the position of each word
  • Allows the model to understand sentence structure and sequence

📌 Exam takeaway: Transformers use positional encoding to retain word order information.

5. Strong Performance on Natural Language Tasks

Transformers are especially effective for:

  • Text translation
  • Text summarization
  • Question answering
  • Chatbots and conversational AI
  • Sentiment analysis

📌 Exam takeaway: Transformers are closely associated with natural language processing workloads.

Why Transformers Are Important in Azure AI

Microsoft Azure AI services rely heavily on Transformer-based models, especially in:

  • Azure OpenAI Service
  • Azure AI Language
  • Conversational AI and copilots
  • Search and knowledge mining

Understanding Transformers helps explain why modern AI solutions are more accurate, context-aware, and scalable.

Transformers vs Earlier Models (High-Level)

FeatureEarlier Models (RNNs/CNNs)Transformers
Sequence processingSequentialParallel
Context handlingLimitedStrong
Long-range dependenciesDifficultEffective
Training speedSlowerFaster
NLP performanceModerateState-of-the-art

📌 Exam focus: You don’t need technical depth — just understand why Transformers are better for language tasks.

Common Exam Pitfalls to Avoid

  • ❌ Thinking Transformers replace all ML models
  • ❌ Assuming Transformers are only for images
  • ❌ Confusing Transformers with traditional rule-based NLP

✅ Remember: Transformers are deep learning models optimized for language and sequence understanding.

Key Exam Summary (Must-Know Points)

If you remember nothing else, remember this:

  • Transformers are deep learning models
  • They rely on self-attention
  • They process data in parallel
  • They are especially effective for natural language processing
  • They power modern AI services in Azure

Go to the Practice Exam Questions for this topic.

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Practice Questions: Identify Features and Uses for Key Phrase Extraction (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to automatically identify the main topics discussed in thousands of customer reviews without determining whether the reviews are positive or negative.

Which NLP capability should be used?

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

Correct Answer: C

Explanation:
Key phrase extraction identifies important topics and concepts in text without analyzing emotional tone, making it ideal for summarizing review content.

Question 2

Which output is most likely returned by a key phrase extraction service?

A. A sentiment score between –1 and 1
B. A list of important words or short phrases
C. A detected language code
D. A classification label

Correct Answer: B

Explanation:
Key phrase extraction returns a list of relevant words or phrases that summarize the main ideas of the text.

Question 3

Which Azure service provides key phrase extraction using prebuilt models?

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

Correct Answer: C

Explanation:
Key phrase extraction is part of Azure AI Language, which offers prebuilt NLP models accessible via APIs.

Question 4

A support team wants to automatically tag incoming support tickets with topics such as billing, login issues, or performance.

Which NLP capability should they use?

A. Named entity recognition
B. Key phrase extraction
C. Sentiment analysis
D. Speech-to-text

Correct Answer: B

Explanation:
Key phrase extraction identifies important topics in unstructured text, making it suitable for tagging and categorization.

Question 5

Which scenario is NOT a typical use of key phrase extraction?

A. Summarizing the main topics of documents
B. Improving document search and indexing
C. Detecting the emotional tone of text
D. Identifying trending discussion topics

Correct Answer: C

Explanation:
Detecting emotional tone is handled by sentiment analysis, not key phrase extraction.

Question 6

Which statement best describes key phrase extraction for the AI-900 exam?

A. It requires labeled training data
B. It extracts names and dates only
C. It uses pretrained models on unstructured text
D. It classifies text into predefined categories

Correct Answer: C

Explanation:
Key phrase extraction uses pretrained NLP models and works directly on unstructured text without training.

Question 7

A multinational company wants to extract key topics from documents written in multiple languages.

Which feature of Azure AI Language supports this requirement?

A. Custom model training
B. Multi-language support
C. Facial recognition
D. Object detection

Correct Answer: B

Explanation:
Azure AI Language supports multiple languages for key phrase extraction, enabling global text analysis.

Question 8

Which NLP capability focuses on identifying specific items such as names, locations, and dates?

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

Correct Answer: D

Explanation:
Entity recognition extracts specific entities, while key phrase extraction focuses on main topics and concepts.

Question 9

A business wants to quickly understand what large volumes of text are about, without reading every document.

Which benefit of key phrase extraction addresses this need?

A. Emotion detection
B. Automatic topic identification
C. Speech recognition
D. Image analysis

Correct Answer: B

Explanation:
Key phrase extraction automatically identifies important topics, allowing rapid understanding of large text collections.

Question 10

Which responsible AI consideration is most relevant when using key phrase extraction?

A. Identity verification
B. Avoiding misinterpretation of extracted phrases
C. Biometric data protection
D. Facial bias detection

Correct Answer: B

Explanation:
Key phrase extraction outputs are contextual summaries, so users must avoid treating them as definitive conclusions.

Exam Tip Recap 🔑

Often paired with search, tagging, and trend analysis

Key phrase extraction = What is this text about?

It does not analyze sentiment

Uses prebuilt models in Azure AI Language

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Identify Features and Uses for Key Phrase Extraction (AI-900 Exam Prep)

Overview

Key phrase extraction is a Natural Language Processing (NLP) capability that identifies the main topics or important terms within unstructured text. In the context of the AI-900: Microsoft Azure AI Fundamentals exam, you are expected to understand what key phrase extraction does, when to use it, and how it differs from other NLP workloads.

In Azure, key phrase extraction is provided through Azure AI Language using prebuilt models, requiring no custom training.

What Is Key Phrase Extraction?

Key phrase extraction answers the question:

“What is this text mainly about?”

It analyzes text and returns a list of relevant words or short phrases that summarize the core ideas.

Example:

“Azure AI provides cloud-based artificial intelligence services for developers.”

Extracted key phrases might include:

  • Azure AI
  • artificial intelligence services
  • cloud-based
  • developers

Core Features of Key Phrase Extraction

1. Automatic Topic Identification

The service automatically identifies:

  • Important concepts
  • Repeated or emphasized terms
  • Meaningful noun phrases

This helps users quickly understand large volumes of text.

2. Works with Unstructured Text

Key phrase extraction can be applied to:

  • Customer reviews
  • Support tickets
  • Emails
  • Social media posts
  • Articles and documents

No formatting or labeling is required.

3. Prebuilt NLP Models

For AI-900 purposes:

  • No model training is required
  • No labeled datasets are needed
  • The service is accessed via API calls or SDKs

This makes it ideal for rapid implementation.

4. Multi-Language Support

Azure AI Language supports multiple languages for key phrase extraction, making it suitable for global applications.

Common Use Cases

Summarizing Customer Feedback

Organizations can extract key phrases from thousands of customer comments to identify:

  • Common complaints
  • Popular features
  • Emerging issues

Search and Indexing

Key phrases can be used to:

  • Improve document search
  • Tag content automatically
  • Enhance content discoverability

Trend and Topic Analysis

By aggregating extracted phrases, businesses can:

  • Identify trending topics
  • Monitor brand mentions
  • Analyze public sentiment themes

Key Phrase Extraction vs Other NLP Workloads

NLP CapabilityPrimary Purpose
Key phrase extractionIdentify main topics in text
Sentiment analysisDetermine emotional tone
Language detectionIdentify the language used
Entity recognitionExtract specific entities (names, dates, locations)

Understanding these distinctions is critical for AI-900 exam questions.

Typical AI-900 Exam Scenarios

You may see questions describing:

  • Analyzing large amounts of feedback text
  • Automatically tagging documents
  • Identifying main discussion points without understanding emotion

Correct answers will reference:

  • Key phrase extraction
  • Azure AI Language
  • Prebuilt NLP models

Responsible AI Considerations

Although key phrase extraction does not directly analyze people, responsible usage still includes:

  • Avoiding misinterpretation of extracted phrases
  • Understanding that output is contextual, not definitive
  • Using extracted phrases as decision support, not final judgment

Key Takeaways for the AI-900 Exam

  • Key phrase extraction identifies important topics, not sentiment
  • It works on unstructured text
  • It uses pretrained models in Azure AI Language
  • It complements other NLP workloads rather than replacing them

A strong grasp of when to use key phrase extraction will help you confidently answer AI-900 questions related to Natural Language Processing workloads.

Go to the Practice Exam Questions for this topic.

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AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Practice Questions: Identify features and uses for entity recognition (AI-900 Exam Prep)

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.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Identify Features and Uses for Entity Recognition (AI-900 Exam Prep)

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.

CapabilityWhat it identifies
Entity recognitionSpecific items (names, places, dates)
Key phrase extractionMain topics and concepts
Sentiment analysisEmotional tone
Language detectionLanguage 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.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Practice Questions: Identify features and uses for sentiment analysis (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of sentiment analysis in Natural Language Processing?

A. To identify people, places, and organizations in text
B. To determine the emotional tone of text
C. To translate text between languages
D. To summarize large documents

Correct Answer: B

Explanation:
Sentiment analysis evaluates the emotional tone or opinion expressed in text, such as positive, negative, neutral, or mixed. Entity recognition, translation, and summarization are different NLP tasks.

Question 2

Which Azure service provides sentiment analysis capabilities?

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

Correct Answer: C

Explanation:
Sentiment analysis is part of Azure AI Language, which provides pretrained NLP models for analyzing text sentiment, key phrases, entities, and more.

Question 3

A company wants to analyze customer reviews to determine whether feedback is positive or negative. Which AI capability should they use?

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

Correct Answer: B

Explanation:
Sentiment analysis is designed to classify text based on emotional tone, making it ideal for customer reviews and feedback analysis.

Question 4

Which sentiment classifications can Azure AI Language return?

A. Happy, Sad, Angry
B. Positive, Negative, Neutral, Mixed
C. True, False, Unknown
D. Approved, Rejected, Pending

Correct Answer: B

Explanation:
Azure sentiment analysis classifies text into positive, negative, neutral, or mixed sentiments.

Question 5

Which additional information is returned with sentiment analysis results?

A. Translation accuracy
B. Confidence scores
C. Named entities
D. Text summaries

Correct Answer: B

Explanation:
Sentiment analysis includes confidence scores, indicating how strongly the model believes the sentiment classification applies.

Question 6

A support team wants to automatically identify angry customer emails for escalation. Which NLP feature is most appropriate?

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

Correct Answer: C

Explanation:
Sentiment analysis helps detect negative or frustrated emotions, enabling automated prioritization of customer support requests.

Question 7

Which scenario is NOT an appropriate use case for sentiment analysis?

A. Measuring public opinion on social media
B. Identifying dissatisfaction in survey responses
C. Extracting product names from reviews
D. Monitoring brand perception

Correct Answer: C

Explanation:
Extracting product names is a task for entity recognition, not sentiment analysis.

Question 8

Does sentiment analysis in Azure AI Language require custom model training?

A. Yes, labeled data is required
B. Yes, but only for large datasets
C. No, it uses pretrained models
D. Only when using multiple languages

Correct Answer: C

Explanation:
Azure AI Language uses pretrained models, allowing sentiment analysis without building or training custom machine learning models.

Question 9

At which levels can sentiment analysis be applied?

A. Document level only
B. Sentence level only
C. Word level only
D. Document and sentence level

Correct Answer: D

Explanation:
Azure sentiment analysis evaluates sentiment at both the document level and sentence level, allowing more detailed insights.

Question 10

A business wants to understand how customers feel about a product, not what the product is. Which NLP capability should be used?

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

Correct Answer: C

Explanation:
Sentiment analysis focuses on emotional tone, while key phrase extraction and entity recognition focus on content and structure.

Final Exam Tip 🎯

For AI-900, always ask yourself:

“Am I being asked about emotion or opinion?”

If the answer is yes → Sentiment analysis

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Identify Features and Uses for Sentiment Analysis (AI-900 Exam Prep)

Overview

Sentiment analysis is a Natural Language Processing (NLP) capability that determines the emotional tone or opinion expressed in text. In the context of the AI-900 exam, sentiment analysis is tested as a foundational NLP workload and is typically associated with scenarios involving customer feedback, reviews, social media posts, and support interactions.

On Azure, sentiment analysis is provided through Azure AI Language, which offers pretrained models that can analyze text without requiring machine learning expertise.

What Is Sentiment Analysis?

Sentiment analysis evaluates text to identify:

  • Overall sentiment (positive, negative, neutral, or mixed)
  • Confidence scores indicating how strongly the sentiment is expressed
  • Sentence-level sentiment (in addition to document-level sentiment)
  • Opinion mining (identifying sentiment about specific aspects, at a high level)

Example:

“The product works great, but the delivery was slow.”

Sentiment analysis can identify:

  • Positive sentiment about the product
  • Negative sentiment about the delivery
  • An overall mixed sentiment for the entire text

Azure Service Used for Sentiment Analysis

Sentiment analysis is a feature of:

Azure AI Language

Part of Azure AI Services, Azure AI Language provides several NLP capabilities, including:

  • Sentiment analysis
  • Key phrase extraction
  • Entity recognition
  • Language detection

For AI-900:

  • No custom model training is required
  • Prebuilt models are used
  • Text can be analyzed via REST APIs or SDKs

Key Features of Sentiment Analysis

1. Sentiment Classification

Text is classified into:

  • Positive
  • Negative
  • Neutral
  • Mixed

This classification applies at both:

  • Document level
  • Sentence level

2. Confidence Scores

Each sentiment classification includes a confidence score, indicating how strongly the model believes the sentiment applies.

Example:

  • Positive: 0.92
  • Neutral: 0.05
  • Negative: 0.03

Higher confidence scores indicate stronger sentiment.

3. Multi-Language Support

Azure AI Language supports sentiment analysis across multiple languages, making it suitable for global applications.

4. Pretrained Models

Sentiment analysis:

  • Uses pretrained AI models
  • Requires no labeled data
  • Can be implemented quickly

This aligns with the AI-900 focus on using AI services rather than building models.

Common Use Cases for Sentiment Analysis

1. Customer Feedback Analysis

Analyze:

  • Product reviews
  • Surveys
  • Net Promoter Score (NPS) comments

Goal: Understand customer satisfaction trends at scale.

2. Social Media Monitoring

Organizations analyze social media posts to:

  • Track brand perception
  • Identify emerging issues
  • Measure reaction to announcements or campaigns

3. Support Ticket Prioritization

Sentiment analysis can help:

  • Identify frustrated or angry customers
  • Escalate negative interactions automatically
  • Improve response times

4. Market Research

Sentiment analysis helps companies understand:

  • Public opinion about competitors
  • Trends in consumer sentiment
  • Product reception after launch

What Sentiment Analysis Is NOT Used For

This distinction is commonly tested on the exam.

TaskCorrect Capability
Extract names or datesEntity recognition
Identify important topicsKey phrase extraction
Translate textTranslation
Detect emotional toneSentiment analysis

Sentiment Analysis vs Related NLP Features

Sentiment Analysis vs Key Phrase Extraction

  • Sentiment analysis: How does the user feel?
  • Key phrase extraction: What is the text about?

Sentiment Analysis vs Entity Recognition

  • Sentiment analysis: Emotional tone
  • Entity recognition: Specific items (people, places, dates)

AI-900 Exam Tips 💡

  • Focus on when to use sentiment analysis, not how to implement it
  • Expect scenario-based questions (customer reviews, feedback, tweets)
  • Remember: Sentiment analysis is part of Azure AI Language
  • No training, tuning, or ML pipelines are required for AI-900

Summary

Sentiment analysis is a core NLP workload that enables organizations to automatically evaluate opinions and emotions in text. For the AI-900 exam, you should understand:

  • What sentiment analysis does
  • Common real-world use cases
  • How it differs from other NLP features
  • That it is delivered through Azure AI Language using pretrained models

Go to the Practice Exam Questions for this topic.

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