Category: AI-900

AI, AI-900, Artificial Intelligence (AI), Computer Vision, Microsoft Certification

Practice Questions: Describe Capabilities of the Azure AI Face Detection Service (AI-900 Exam Prep)

Practice Exam Questions

Question 1

A company wants to detect whether human faces appear in uploaded images and draw bounding boxes around them. The solution must not identify individuals.

Which Azure service should be used?

A. Azure Custom Vision
B. Azure AI Vision image classification
C. Azure AI Face detection
D. Azure OpenAI Service

Correct Answer: C

Explanation:
Azure AI Face detection is designed to detect faces and return their locations without identifying individuals. This aligns with privacy requirements and AI-900 expectations.

Question 2

Which task is supported by Azure AI Face detection?

A. Verifying a person’s identity against a database
B. Detecting the presence of human faces in an image
C. Training a custom facial recognition model
D. Authenticating users using facial biometrics

Correct Answer: B

Explanation:
Azure AI Face detection can detect faces and analyze facial attributes, but it does not perform identity verification or authentication.

Question 3

What type of information can Azure AI Face detection return for each detected face?

A. Person’s name and ID
B. Bounding box and facial attributes
C. Social media profile matches
D. Voice and speech characteristics

Correct Answer: B

Explanation:
The service returns face location (bounding box) and facial attributes such as estimated age or expression, not personal identity data.

Question 4

A scenario requires estimating whether people in an image appear to be smiling.

Which Azure AI Face detection capability supports this requirement?

A. Face identification
B. Facial attribute analysis
C. Image classification
D. Object detection

Correct Answer: B

Explanation:
Facial attribute analysis provides descriptive information such as facial expression, including whether a face appears to be smiling.

Question 5

Which statement best describes Azure AI Face detection for the AI-900 exam?

A. It requires training a custom dataset
B. It identifies known individuals in photos
C. It uses prebuilt models to analyze faces
D. It can only analyze video streams

Correct Answer: C

Explanation:
Azure AI Face detection uses pretrained models and requires no custom training, which is a key exam concept.

Question 6

A developer wants to count how many people appear in a group photo.

Which Azure AI service capability should be used?

A. OCR
B. Image tagging
C. Face detection
D. Image classification

Correct Answer: C

Explanation:
Face detection can identify multiple faces in a single image, making it suitable for counting people.

Question 7

Why is Azure AI Face detection closely associated with Responsible AI principles?

A. It uses unsupervised learning
B. It processes sensitive human biometric data
C. It requires large datasets
D. It supports only public images

Correct Answer: B

Explanation:
Facial data is considered sensitive personal data, so privacy, fairness, and transparency are especially important.

Question 8

Which scenario would be inappropriate for Azure AI Face detection?

A. Detecting faces in event photos
B. Estimating facial expressions
C. Identifying a person by name from an image
D. Drawing bounding boxes around faces

Correct Answer: C

Explanation:
Azure AI Face detection does not identify individuals. Identity recognition is outside the scope of AI-900 and restricted for ethical reasons.

Question 9

Which principle ensures users are informed when facial analysis is being used?

A. Reliability
B. Transparency
C. Inclusiveness
D. Sustainability

Correct Answer: B

Explanation:
Transparency requires that people understand when and how AI systems, such as facial detection, are being used.

Question 10

When comparing Azure AI Face detection with object detection, which statement is correct?

A. Object detection returns facial attributes
B. Face detection identifies any object in an image
C. Face detection focuses specifically on human faces
D. Both services identify individuals

Correct Answer: C

Explanation:
Face detection is specialized for human faces, while object detection identifies general objects like cars, animals, or furniture.

Exam Tip Recap 🔑

  • Face detection ≠ face recognition
  • Detects faces, locations, and attributes
  • Uses prebuilt models
  • Strong ties to Responsible AI

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

AI, AI-900, Artificial Intelligence (AI), Computer Vision, Microsoft Certification

Describe Capabilities of the Azure AI Face Detection Service (AI-900 Exam Prep)

Overview

The Azure AI Face Detection service (part of Azure AI Vision) provides prebuilt computer vision capabilities to detect human faces in images and return structured information about those faces. For the AI-900: Microsoft Azure AI Fundamentals exam, the focus is on understanding what the service can do, what it cannot do, and how it aligns with Responsible AI principles.

This service uses pretrained models and can be accessed through REST APIs or SDKs without building or training a custom machine learning model.

What Is Face Detection (at the AI-900 level)?

Face detection answers the question:

“Is there a human face in this image, and what are its characteristics?”

It does not answer:

“Who is this person?”

This distinction is critical for the AI-900 exam.

Core Capabilities of Azure AI Face Detection

1. Face Detection

The service can:

  • Detect one or more human faces in an image
  • Return the location of each face using bounding boxes
  • Assign a confidence score to each detected face

This capability is commonly used for:

  • Photo moderation
  • Counting people in images
  • Identifying whether faces are present at all

2. Facial Attribute Analysis

For each detected face, the service can analyze and return attributes such as:

  • Estimated age range
  • Facial expression (for example, neutral or smiling)
  • Head pose (orientation of the face)
  • Glasses or accessories
  • Hair-related attributes

These attributes are descriptive and probabilistic, not definitive.

3. Multiple Face Detection

Azure AI Face Detection can:

  • Detect multiple faces in a single image
  • Return attributes for each detected face independently

This is useful in scenarios like:

  • Group photos
  • Crowd analysis
  • Event imagery

What Azure AI Face Detection Does NOT Do

Understanding limitations is frequently tested on AI-900.

The service does NOT:

  • Identify or verify individuals
  • Perform facial recognition for authentication
  • Match faces against a database of known people

Any functionality related to identity recognition falls outside the scope of AI-900 and is intentionally restricted due to privacy and ethical considerations.

Responsible AI Considerations

Facial analysis involves human biometric data, so Microsoft strongly emphasizes Responsible AI principles.

Key considerations include:

  • Privacy: Faces are sensitive personal data
  • Fairness: Models must work consistently across different demographics
  • Transparency: Users should be informed when facial analysis is used
  • Accountability: Humans remain responsible for how outputs are used

For AI-900, you are expected to recognize that facial detection requires extra care compared to other vision tasks like object detection or OCR.

Common AI-900 Exam Scenarios

You may see questions that describe:

  • Detecting whether people appear in an image
  • Returning bounding boxes around faces
  • Analyzing facial attributes without identifying individuals

Correct answers will typically reference:

  • Azure AI Face Detection
  • Prebuilt models
  • No custom training required

Azure AI Face Detection vs Other Vision Capabilities

CapabilityPurpose
Image classificationAssigns a single label to an image
Object detectionIdentifies objects and their locations
OCRExtracts text from images
Face detectionDetects faces and analyzes attributes

Key Takeaways for the AI-900 Exam

  • Azure AI Face Detection detects faces, not identities
  • It returns locations and attributes, not names
  • It uses pretrained models with no training required
  • Facial analysis requires Responsible AI awareness

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.

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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

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Practice Questions: Identify Features and Uses for Language Modeling (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of a language model in natural language processing?

A. To detect objects in images
B. To classify numerical data
C. To predict and generate sequences of text
D. To translate speech into audio

Correct Answer: C

Explanation:
Language models are designed to predict and generate text based on learned language patterns. They analyze sequences of words to understand context and produce meaningful text.

Question 2

Which scenario is the best example of a language modeling workload?

A. Detecting faces in an image
B. Generating a human-like response in a chatbot
C. Identifying key phrases in a document
D. Extracting entities such as names and locations

Correct Answer: B

Explanation:
Chatbots rely on language models to understand user input and generate natural language responses, which is a core language modeling capability.

Question 3

A company wants an AI system that can automatically complete sentences as users type. Which NLP capability is required?

A. Sentiment analysis
B. Entity recognition
C. Language modeling
D. Optical character recognition

Correct Answer: C

Explanation:
Sentence and text completion depend on predicting the next word or phrase, which is a fundamental feature of language modeling.

Question 4

Which feature distinguishes modern language models from earlier rule-based NLP systems?

A. They rely only on predefined grammar rules
B. They can understand context across multiple words or sentences
C. They only work with structured data
D. They require manual labeling of every sentence

Correct Answer: B

Explanation:
Modern language models use context to generate coherent responses, allowing them to understand meaning beyond individual words.

Question 5

Which Azure service provides access to advanced pretrained language models for text generation and conversational AI?

A. Azure AI Vision
B. Azure AI Language
C. Azure OpenAI Service
D. Azure Form Recognizer

Correct Answer: C

Explanation:
The Azure OpenAI Service provides access to large pretrained language models that support text generation, chat, summarization, and reasoning.

Question 6

A customer support system automatically answers user questions using natural language. Which AI capability is primarily being used?

A. Object detection
B. Language modeling
C. Key phrase extraction
D. Speech synthesis

Correct Answer: B

Explanation:
Automatically generating answers in natural language relies on language modeling, especially for conversational and question-answering scenarios.

Question 7

Which task is least likely to use language modeling?

A. Generating a summary of a document
B. Translating text between languages
C. Detecting the sentiment of a sentence
D. Producing a chatbot response

Correct Answer: C

Explanation:
Sentiment analysis focuses on identifying emotional tone, not generating or predicting text. The other options rely heavily on language modeling.

Question 8

Why are pretrained language models commonly used in Azure AI solutions?

A. They eliminate the need for any data
B. They require less storage than traditional models
C. They can be used immediately without custom training
D. They only support English language text

Correct Answer: C

Explanation:
Pretrained models are already trained on large datasets and can be used out of the box, which aligns with Azure’s AI service approach.

Question 9

Which statement best describes how language models generate text?

A. By randomly selecting words from a dictionary
B. By applying fixed grammatical rules
C. By predicting the most likely next word in a sequence
D. By translating text into numerical values only

Correct Answer: C

Explanation:
Language models generate text by calculating probabilities of word sequences and selecting the most likely continuation.

Question 10

A solution needs to create readable paragraphs based on a short prompt provided by a user. Which AI capability should be used?

A. Optical character recognition
B. Speech recognition
C. Language modeling
D. Image classification

Correct Answer: C

Explanation:
Generating paragraphs from a prompt is a classic language modeling use case involving text prediction and generation.

Quick Exam Tip

If the question involves:

  • Text generation
  • Chatbots
  • Predicting or completing text
  • Understanding context in language

👉 It’s a good chance it involves Language Modeling

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

Identify Features and Uses for Language Modeling (AI-900 Exam Prep)

Overview

Language modeling is a core concept in Natural Language Processing (NLP) that focuses on enabling machines to understand, generate, and predict human language. In the context of the AI-900 exam, language modeling is not about building models from scratch, but about recognizing what language models do, what problems they solve, and how Azure provides access to them.

Language models power many modern AI experiences, including chatbots, text generation, summarization, translation, and question answering.

What Is a Language Model?

A language model is a type of AI model that learns patterns in language so it can:

  • Predict the next word or token in a sequence
  • Understand context and meaning
  • Generate coherent and contextually relevant text

At a fundamental level, language models calculate the probability of word sequences, which allows them to both interpret and generate language.

Key Features of Language Modeling

1. Text Prediction and Generation

Language models can:

  • Predict the next word in a sentence
  • Generate full sentences, paragraphs, or documents
  • Produce human-like responses in conversations

Example:

“The weather today is very…” → sunny

2. Context Awareness

Modern language models (especially transformer-based models) consider context, not just individual words.

This allows them to:

  • Understand sentence meaning
  • Maintain coherence across multiple sentences
  • Respond appropriately based on prior text

3. Natural Language Understanding and Generation

Language models support both:

  • Understanding text (reading and interpreting meaning)
  • Generating text (writing responses, summaries, or explanations)

This dual capability is central to many NLP workloads.

4. Pretrained Models

In Azure, language modeling typically relies on pretrained models, meaning:

  • No custom training is required
  • Models are already trained on large text datasets
  • Users can immediately apply them to common NLP tasks

This aligns with the AI-900 focus on consuming AI services, not building models.

Common Uses of Language Modeling

1. Chatbots and Virtual Assistants

Language models enable conversational AI by:

  • Understanding user input
  • Generating natural responses
  • Maintaining conversation context

Azure Example:
Chatbots built using Azure OpenAI Service or language-based Azure AI services.

2. Text Completion and Content Generation

Language models can:

  • Auto-complete sentences
  • Generate emails, reports, or documentation
  • Assist with creative writing or code comments

3. Question Answering

Language models can:

  • Interpret natural language questions
  • Generate relevant answers based on context or provided data

This is commonly used in:

  • Help desks
  • Knowledge bases
  • Internal support tools

4. Text Summarization

Language models can:

  • Condense long documents
  • Extract key points
  • Provide concise summaries

This helps users quickly understand large volumes of text.

5. Language Translation and Adaptation

While translation is often a separate NLP workload, language models:

  • Understand sentence structure
  • Preserve meaning across languages
  • Adapt phrasing naturally

Language Modeling in Azure

In Azure, language modeling capabilities are available through services such as:

Azure OpenAI Service

  • Provides access to powerful large language models
  • Supports text generation, chat, summarization, and reasoning tasks
  • Uses pretrained transformer-based models

Azure AI Language

  • Focuses on structured NLP tasks
  • Complements language modeling with features like sentiment analysis and entity recognition

For AI-900, it’s important to recognize what language models enable, not the underlying implementation details.

Language Modeling vs Other NLP Tasks (Exam Tip)

NLP TaskFocus
Sentiment analysisEmotional tone
Entity recognitionIdentifying names, places, organizations
Key phrase extractionImportant terms
Language modelingUnderstanding and generating language

If the question involves predicting, generating, or responding with text, language modeling is likely the correct concept.

Why Language Modeling Matters for AI-900

Microsoft includes language modeling in AI-900 to ensure candidates understand:

  • How modern AI systems interact with human language
  • Why conversational AI is possible
  • How Azure provides ready-to-use NLP capabilities

You are not expected to train models — only to identify features, uses, and scenarios.

Exam Takeaway

If a question mentions:

  • Text generation
  • Conversational AI
  • Predicting words or sentences
  • Understanding context in language

👉 Think Language Modeling

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