Artificial Intelligence (AI), AI, Microsoft Certification, AI Governance, AI-901

Identify scenarios for common AI workloads, Including Generative and Agentic AI, Text Analysis, Speech, Computer Vision, and Information Extraction (AI-901 Exam Prep)

 
This post is a part of the AI-901: Microsoft Azure AI Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Identify AI concepts and capabilities (40–45%)
   --> Identify AI workloads
      --> Identify scenarios for common AI workloads, Including Generative and Agentic AI, Text Analysis, Speech, Computer Vision, and Information Extraction

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Understanding common AI workloads is one of the foundational concepts in artificial intelligence and a major focus area of the AI-901 certification exam. Microsoft expects candidates to recognize different types of AI workloads and identify appropriate real-world scenarios for each.

This topic falls under the “Identify AI workloads” section of the exam objectives.

What Is an AI Workload?

An AI workload is a category of AI tasks designed to solve a particular type of problem.

Different workloads specialize in processing different types of data such as:

  • Text
  • Speech
  • Images
  • Documents
  • Audio
  • Video

Understanding AI workloads helps organizations choose the correct AI technologies for business solutions.

Major AI Workloads for AI-901

For the AI-901 exam, you should understand these common AI workloads:

  • Generative AI
  • Agentic AI
  • Text analysis
  • Speech AI
  • Computer vision
  • Information extraction

Generative AI

Generative AI creates new content based on patterns learned from training data.

Common Outputs

  • Text
  • Images
  • Audio
  • Video
  • Code

Common Scenarios

  • AI chatbots
  • Content creation
  • Email drafting
  • Code generation
  • Image generation
  • Text summarization

Example

A marketing team uses AI to generate product descriptions automatically.

Large Language Models (LLMs)

Many generative AI systems use Large Language Models (LLMs).

LLMs are trained on massive text datasets and can:

  • Answer questions
  • Summarize content
  • Generate text
  • Translate languages
  • Assist with coding

Example

An AI assistant generates meeting summaries from conversation transcripts.

Agentic AI

Agentic AI refers to AI systems that can autonomously plan, reason, and take actions to accomplish goals.

Agentic AI systems may:

  • Make decisions
  • Perform multi-step tasks
  • Use tools
  • Interact with applications
  • Adapt based on feedback

Unlike simple chatbots, agentic AI systems can perform actions and workflows.

Agentic AI Scenarios

Examples

  • AI travel planning assistants
  • Autonomous customer support agents
  • AI workflow automation systems
  • AI research assistants
  • Scheduling assistants

Example

An AI assistant receives a request to schedule a meeting, checks calendars, sends invitations, and updates schedules automatically.

Text Analysis

Text analysis is an AI workload focused on understanding and processing written language.

Text analysis is part of Natural Language Processing (NLP).

Common Capabilities

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

Sentiment Analysis

Sentiment analysis identifies emotional tone in text.

Example Scenarios

  • Product review analysis
  • Social media monitoring
  • Customer feedback analysis

Example

An organization analyzes customer reviews to determine whether feedback is positive or negative.

Key Phrase Extraction

Key phrase extraction identifies important terms or phrases in text.

Example Scenarios

  • Document summarization
  • Search indexing
  • Topic identification

Example

An AI system extracts important keywords from support tickets.

Language Detection

Language detection identifies the language used in text.

Example Scenarios

  • Multilingual applications
  • Translation routing
  • Global customer support

Example

A website detects whether incoming text is English, Spanish, or French.

Named Entity Recognition (NER)

NER identifies important entities in text such as:

  • People
  • Organizations
  • Locations
  • Dates

Example

An AI system extracts company names and locations from contracts.

Speech AI

Speech AI works with spoken language and audio.

Common Capabilities

  • Speech-to-text
  • Text-to-speech
  • Speech translation
  • Speaker recognition

Speech-to-Text

Speech-to-text converts spoken audio into written text.

Example Scenarios

  • Voice transcription
  • Meeting captions
  • Voice assistants

Example

A meeting platform generates live captions during conferences.

Text-to-Speech

Text-to-speech converts written text into spoken audio.

Example Scenarios

  • Accessibility tools
  • Virtual assistants
  • Audiobooks
  • Navigation systems

Example

A navigation app reads driving directions aloud.

Speech Translation

Speech translation converts spoken language into another language.

Example Scenarios

  • International meetings
  • Travel applications
  • Multilingual support systems

Example

A conference tool translates spoken English into Spanish in real time.

Computer Vision

Computer vision enables AI systems to analyze images and video.

Common Capabilities

  • Image classification
  • Object detection
  • Facial recognition
  • OCR
  • Image tagging

Image Classification

Image classification identifies the contents of an image.

Example Scenarios

  • Medical image analysis
  • Product categorization
  • Wildlife monitoring

Example

An AI system identifies whether an image contains a cat or a dog.

Object Detection

Object detection identifies and locates objects within an image.

Example Scenarios

  • Traffic monitoring
  • Security surveillance
  • Manufacturing inspection

Example

A self-driving car detects pedestrians and vehicles.

Optical Character Recognition (OCR)

OCR extracts text from images or scanned documents.

Example Scenarios

  • Invoice processing
  • Form digitization
  • Receipt scanning

Example

An AI system extracts totals and dates from receipts.

Facial Recognition

Facial recognition identifies or verifies people using facial features.

Example Scenarios

  • Building access systems
  • Smartphone authentication
  • Security systems

Example

A mobile phone unlocks using facial recognition.

Information Extraction

Information extraction identifies and retrieves structured information from unstructured content.

This workload often combines:

  • OCR
  • NLP
  • Document analysis

Information Extraction Scenarios

Examples

  • Invoice processing
  • Contract analysis
  • Insurance claims processing
  • Healthcare form processing

Example

An AI system extracts invoice numbers, dates, and totals from scanned invoices automatically.

Structured vs. Unstructured Data

AI workloads often process unstructured data.

Structured DataUnstructured Data
TablesDocuments
DatabasesImages
SpreadsheetsAudio
Defined formatsVideos

Many AI workloads specialize in converting unstructured data into structured information.

Choosing the Correct AI Workload

Understanding the business problem helps determine the correct AI workload.

ScenarioAppropriate Workload
Generate contentGenerative AI
Perform autonomous tasksAgentic AI
Analyze written reviewsText analysis
Convert speech to textSpeech AI
Analyze imagesComputer vision
Extract data from formsInformation extraction

Real-World Examples

Scenario 1: Customer Support Chatbot

Goal

Answer customer questions naturally.

Appropriate Workload

Generative AI

Scenario 2: AI Scheduling Assistant

Goal

Manage appointments automatically.

Appropriate Workload

Agentic AI

Scenario 3: Review Analysis System

Goal

Determine customer sentiment.

Appropriate Workload

Text analysis

Scenario 4: Live Meeting Captions

Goal

Convert speech into text in real time.

Appropriate Workload

Speech AI

Scenario 5: Self-Driving Vehicle

Goal

Detect objects and surroundings.

Appropriate Workload

Computer vision

Scenario 6: Invoice Data Extraction

Goal

Extract invoice information automatically.

Appropriate Workload

Information extraction

Azure AI Services for Common Workloads

Microsoft Azure AI Services provide prebuilt tools for many AI workloads, including:

  • Azure AI Language
  • Azure AI Speech
  • Azure AI Vision
  • Azure AI Document Intelligence
  • Azure OpenAI Service

These services help organizations build AI solutions without creating models from scratch.

Responsible AI Considerations

All AI workloads should follow Responsible AI principles, including:

  • Fairness
  • Privacy
  • Transparency
  • Reliability
  • Inclusiveness
  • Accountability

Organizations should ensure AI systems are used ethically and safely.

Important AI-901 Exam Tips

For the exam, remember these key points:

  • Generative AI creates new content.
  • Agentic AI can autonomously perform tasks and workflows.
  • Text analysis processes written language.
  • Speech AI works with spoken language and audio.
  • Computer vision processes images and video.
  • OCR extracts text from images.
  • Information extraction converts unstructured data into structured information.
  • Sentiment analysis determines emotional tone in text.
  • Named Entity Recognition identifies important entities in text.

Quick Knowledge Check

Question 1

Which AI workload is best for generating marketing content?

Answer

Generative AI.

Question 2

Which AI workload converts spoken language into written text?

Answer

Speech AI.

Question 3

What does OCR do?

Answer

Extracts text from images or scanned documents.

Question 4

Which workload is designed to autonomously complete tasks and workflows?

Answer

Agentic AI.

Practice Exam Questions

Question 1

A company wants an AI system that can automatically generate marketing emails and product descriptions.

Which AI workload is MOST appropriate?

A. Computer vision
B. Generative AI
C. OCR
D. Regression analysis

Correct Answer

B. Generative AI

Explanation

Generative AI creates new content such as text, images, audio, and code based on learned patterns.

Why the Other Answers Are Incorrect

A. Computer vision

Computer vision analyzes images and video.

C. OCR

OCR extracts text from images.

D. Regression analysis

Regression predicts numeric values.

Question 2

An organization wants an AI assistant that can schedule meetings, send invitations, and update calendars automatically.

Which AI workload BEST fits this scenario?

A. Speech AI
B. Agentic AI
C. Clustering
D. OCR

Correct Answer

B. Agentic AI

Explanation

Agentic AI systems can autonomously perform multi-step tasks, make decisions, and interact with tools or applications.

Why the Other Answers Are Incorrect

A. Speech AI

Speech AI processes spoken language.

C. Clustering

Clustering groups similar data.

D. OCR

OCR extracts text from images.

Question 3

Which AI workload is MOST appropriate for determining whether customer reviews are positive or negative?

A. Sentiment analysis
B. Object detection
C. Regression
D. Facial recognition

Correct Answer

A. Sentiment analysis

Explanation

Sentiment analysis is a text analysis capability that identifies emotional tone in written text.

Why the Other Answers Are Incorrect

B. Object detection

Object detection identifies objects in images.

C. Regression

Regression predicts numeric values.

D. Facial recognition

Facial recognition analyzes faces in images or video.

Question 4

A company needs to convert spoken customer service calls into written transcripts.

Which AI workload should be used?

A. Computer vision
B. Speech-to-text
C. OCR
D. Recommendation system

Correct Answer

B. Speech-to-text

Explanation

Speech-to-text converts spoken audio into written text.

Why the Other Answers Are Incorrect

A. Computer vision

Computer vision processes images and video.

C. OCR

OCR extracts text from images, not audio.

D. Recommendation system

Recommendation systems suggest items to users.

Question 5

Which AI workload is MOST appropriate for identifying objects such as cars and pedestrians in traffic camera footage?

A. Text analysis
B. Object detection
C. Speech translation
D. Key phrase extraction

Correct Answer

B. Object detection

Explanation

Object detection identifies and locates objects within images or video.

Why the Other Answers Are Incorrect

A. Text analysis

Text analysis processes written language.

C. Speech translation

Speech translation converts spoken language between languages.

D. Key phrase extraction

Key phrase extraction identifies important terms in text.

Question 6

What is the PRIMARY purpose of OCR?

A. Translating spoken language
B. Extracting text from images or scanned documents
C. Detecting emotions in speech
D. Generating new images

Correct Answer

B. Extracting text from images or scanned documents

Explanation

Optical Character Recognition (OCR) converts printed or handwritten text in images into machine-readable text.

Why the Other Answers Are Incorrect

A. Translating spoken language

This is speech translation.

C. Detecting emotions in speech

This is speech or sentiment analysis.

D. Generating new images

This is a generative AI capability.

Question 7

Which workload is MOST associated with analyzing and processing human language?

A. Natural Language Processing (NLP)
B. Computer vision
C. Regression
D. Clustering

Correct Answer

A. Natural Language Processing (NLP)

Explanation

NLP focuses on understanding, analyzing, and generating human language.

Why the Other Answers Are Incorrect

B. Computer vision

Computer vision works with images and video.

C. Regression

Regression predicts numeric values.

D. Clustering

Clustering groups similar items.

Question 8

A business wants to automatically extract invoice numbers, totals, and dates from scanned invoices.

Which AI workload is MOST appropriate?

A. Recommendation system
B. Information extraction
C. Speech recognition
D. Regression

Correct Answer

B. Information extraction

Explanation

Information extraction retrieves structured information from unstructured documents and often combines OCR and NLP technologies.

Why the Other Answers Are Incorrect

A. Recommendation system

Recommendation systems suggest items.

C. Speech recognition

Speech recognition processes audio.

D. Regression

Regression predicts numbers rather than extracting document data.

Question 9

Which scenario BEST represents a computer vision workload?

A. Translating English text into Spanish
B. Detecting defects on a manufacturing assembly line using cameras
C. Summarizing documents automatically
D. Predicting monthly sales revenue

Correct Answer

B. Detecting defects on a manufacturing assembly line using cameras

Explanation

Computer vision systems analyze visual content such as images and video to identify objects, defects, and patterns.

Why the Other Answers Are Incorrect

A. Translating English text into Spanish

This is an NLP task.

C. Summarizing documents automatically

This is a generative AI or NLP task.

D. Predicting monthly sales revenue

This is a regression task.

Question 10

Which statement BEST describes agentic AI?

A. AI systems that only classify images
B. AI systems that autonomously perform tasks and make decisions
C. AI systems that store relational databases
D. AI systems that only process audio recordings

Correct Answer

B. AI systems that autonomously perform tasks and make decisions

Explanation

Agentic AI systems can reason, plan, interact with tools, and complete multi-step workflows with limited human intervention.

Why the Other Answers Are Incorrect

A. AI systems that only classify images

This describes computer vision tasks.

C. AI systems that store relational databases

Databases are not AI workloads.

D. AI systems that only process audio recordings

Speech AI handles audio processing, not autonomous task execution.

Final Thoughts

Understanding common AI workloads is essential for the AI-901 certification exam and for designing effective AI solutions. Microsoft expects candidates to recognize how different AI technologies solve different business problems and when each workload is most appropriate.

These foundational concepts help build a strong understanding of modern AI systems and Azure AI services.

Go to the AI-901 Exam Prep Hub main page

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