Tag: Entity Extraction

AI, AI-103, Azure AI, Microsoft Certification, Natural Language Processing (NLP)

Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools (AI-103 Exam Prep)

 
This post is a part of the AI-103: Develop AI Apps and Agents on Azure Exam Prep Hub. 
This topic falls under these sections:
Implement text analysis solutions (10–15%)
   --> Apply language model text analysis
      --> Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools

Note that there are 10 practice questions (with answers and explanations) at the end of each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available from the hub's main page below the exam topics section.

Introduction

Modern AI applications increasingly rely on language models to transform unstructured text into structured, actionable information. Organizations use generative AI systems to:

  • Extract entities
  • Detect topics
  • Generate summaries
  • Produce structured JSON outputs
  • Automate workflows
  • Enrich search and analytics systems

For the AI-103 certification exam, you should understand how to implement text analysis workflows using:

  • Generative prompting
  • Multimodal and language models
  • Structured outputs
  • Azure AI Foundry tools
  • Prompt orchestration
  • Responsible AI practices

This topic falls under:

“Apply language model text analysis”

What Is Text Analysis?

Definition

Text analysis is the process of extracting meaningful information from unstructured text.

Examples include:

  • Entity extraction
  • Topic classification
  • Sentiment analysis
  • Summarization
  • Categorization
  • Structured data generation

Why Generative AI Improves Text Analysis

Traditional NLP systems often relied on:

  • Rule-based processing
  • Fixed schemas
  • Pretrained classifiers

Generative AI systems provide:

  • Flexible extraction
  • Contextual understanding
  • Natural language reasoning
  • Dynamic schema generation
  • Few-shot adaptability

Common Text Analysis Tasks

Entity Extraction

Identifying important entities within text.

Examples:

  • Names
  • Organizations
  • Dates
  • Locations
  • Products
  • Financial values

Example Entity Extraction

Input:

Contoso signed a contract with Fabrikam on March 5, 2026.

Extracted entities:

{
  "organizations": [
    "Contoso",
    "Fabrikam"
  ],
  "date": "March 5, 2026"
}

Topic Extraction

What Is Topic Extraction?

Topic extraction identifies the primary themes discussed within text.

Example Topics

Document:

The company discussed quarterly cloud migration costs and AI infrastructure scaling.

Detected topics:

  • Cloud computing
  • AI infrastructure
  • Financial operations

Summarization

What Is Summarization?

Summarization condenses large amounts of text into shorter, meaningful summaries.

Types of Summaries

Extractive Summarization

Selects important text directly from the source.

Abstractive Summarization

Generates new language-based summaries.

Generative AI commonly uses abstractive summarization.

Example Summary Prompt

Summarize this customer support conversation in three sentences.

Structured JSON Outputs

Why Structured Outputs Matter

Structured outputs improve:

  • Automation
  • API integration
  • Data pipelines
  • Analytics
  • Workflow orchestration

Example Structured Output

{
  "customer_sentiment": "negative",
  "issue_type": "billing",
  "priority": "high"
}

Prompt Engineering for Text Analysis

Why Prompt Engineering Matters

Prompts strongly influence:

  • Extraction quality
  • Consistency
  • Formatting
  • Hallucination frequency

Example Entity Prompt

Extract all people, organizations, and dates from the following text.

Example JSON Prompt

Return the output strictly as valid JSON.

Example Topic Classification Prompt

Identify the top three business topics discussed in this document.

Few-Shot Prompting

What Is Few-Shot Prompting?

Few-shot prompting provides examples within prompts.

Example

Input: "Invoice overdue for 45 days"
Output:
{
  "category": "accounts receivable"
}

Few-shot prompting improves consistency and accuracy.

Chain-of-Thought Reasoning

Some workflows encourage reasoning before output generation.

Example:

Analyze the text step-by-step before generating the final JSON output.

Structured Output Validation

Generated JSON should be validated to ensure:

  • Proper formatting
  • Required fields
  • Valid schema structure

Example Validation Concerns

Potential issues:

  • Missing fields
  • Invalid JSON syntax
  • Hallucinated values
  • Unexpected schema changes

Hallucinations in Text Analysis

What Are Hallucinations?

Hallucinations occur when models:

  • Invent entities
  • Create unsupported summaries
  • Generate incorrect classifications

Example Hallucination

Input:

Meeting scheduled for Tuesday.

Incorrect output:

{
  "location": "New York"
}

The location was never mentioned.

Reducing Hallucinations

Strategies include:

  • Grounded prompts
  • Retrieval augmentation
  • Schema validation
  • Confidence scoring
  • Human review
  • Explicit formatting instructions

Retrieval-Augmented Generation (RAG)

What Is RAG?

RAG combines:

  • Retrieval systems
  • Vector search
  • Generative models

to improve grounding and reduce hallucinations.

Example RAG Workflow

  1. User submits question
  2. Relevant documents retrieved
  3. LLM analyzes retrieved content
  4. Structured output generated

Azure AI Foundry

Microsoft provides:
Azure AI Foundry

to help build and orchestrate AI workflows.

Foundry Capabilities

Azure AI Foundry supports:

  • Prompt flows
  • Model orchestration
  • Evaluations
  • Safety testing
  • Workflow automation
  • AI experimentation

Prompt Flows

What Are Prompt Flows?

Prompt flows visually orchestrate:

  • Inputs
  • LLM calls
  • Validation steps
  • Tool integrations
  • Output processing

Example Prompt Flow

  1. Receive document
  2. Extract entities
  3. Classify topics
  4. Generate summary
  5. Return JSON response

Multi-Step Text Analysis Pipelines

Organizations commonly chain multiple operations:

  • OCR
  • Summarization
  • Classification
  • Translation
  • Entity extraction

Example Enterprise Workflow

  1. Upload support ticket
  2. Detect language
  3. Extract entities
  4. Summarize issue
  5. Generate structured JSON
  6. Route to support queue

Azure OpenAI Service

Azure OpenAI Service

supports:

  • Generative prompting
  • Structured outputs
  • Summarization
  • Topic extraction
  • Entity extraction

Azure AI Language

Azure AI Language

supports:

  • Named entity recognition
  • Classification
  • Summarization
  • Sentiment analysis

Azure AI Search

Azure AI Search

supports:

  • Vector search
  • Hybrid search
  • Retrieval workflows
  • RAG architectures

Azure Functions

Azure Functions

commonly orchestrates:

  • Text pipelines
  • Event triggers
  • Automated workflows

Security and Responsible AI

Text analysis systems must handle:

  • Sensitive data
  • PII
  • Confidential information
  • Harmful prompts

Responsible AI Considerations

Organizations should:

  • Validate outputs
  • Monitor hallucinations
  • Protect privacy
  • Audit workflows
  • Apply content filtering

Privacy Considerations

Text may contain:

  • Personal information
  • Financial data
  • Medical information
  • Corporate secrets

Organizations should:

  • Encrypt data
  • Restrict access
  • Mask sensitive fields

Human-in-the-Loop Review

Human review may be necessary for:

  • Legal workflows
  • Healthcare systems
  • Financial reporting
  • High-risk classifications

Observability and Monitoring

Production systems should monitor:

  • Latency
  • Token usage
  • Hallucination frequency
  • JSON validation failures
  • Prompt injection attempts
  • Cost
  • Throughput

Cost Optimization

Generative AI pipelines can become expensive.

Optimization strategies include:

  • Shorter prompts
  • Chunking large documents
  • Smaller models where appropriate
  • Caching results
  • Batch processing

Example Structured Extraction Workflow

A legal firm may:

  1. Upload contracts
  2. Extract entities
  3. Detect clauses
  4. Generate summaries
  5. Produce structured JSON metadata
  6. Store searchable outputs

This demonstrates:

  • Entity extraction
  • Summarization
  • Structured outputs
  • Workflow orchestration

Best Practices for Text Analysis Workflows

Use Explicit Prompt Instructions

Improve consistency and formatting.

Validate JSON Outputs

Prevent downstream parsing failures.

Ground Responses in Source Data

Reduce hallucinations.

Use Multi-Step Pipelines

Separate extraction, classification, and summarization stages.

Monitor Hallucinations

Track unsupported outputs.

Protect Sensitive Data

Apply privacy and security controls.

Support Human Review

Especially for high-risk workflows.

Exam Tips for AI-103

For the AI-103 exam, remember these important concepts:

  • Entity extraction identifies structured information within text.
  • Topic extraction identifies major themes.
  • Summarization condenses large text into concise outputs.
  • Structured JSON outputs improve automation and integrations.
  • Prompt engineering strongly affects extraction quality.
  • Few-shot prompting improves consistency.
  • Hallucinations generate unsupported or incorrect outputs.
  • RAG improves grounding using retrieved documents.
  • Azure AI Foundry supports prompt flows and orchestration.
  • Azure OpenAI Service supports generative text analysis workflows.
  • JSON validation is important for reliable downstream processing.

Practice Exam Questions

Question 1

What is the purpose of entity extraction?

A. Compressing text files
B. Identifying structured information such as names and dates
C. Encrypting JSON outputs
D. Scaling databases dynamically

Answer

B. Identifying structured information such as names and dates

Explanation

Entity extraction identifies meaningful structured information within text.

Question 2

What is topic extraction?

A. Compressing prompts
B. Removing hallucinations automatically
C. Encrypting documents
D. Identifying major themes discussed within text

Answer

D. Identifying major themes discussed within text

Explanation

Topic extraction identifies the primary subjects or themes in content.

Question 3

Why are structured JSON outputs useful?

A. They simplify automation and system integration
B. They eliminate OCR workflows
C. They reduce internet bandwidth usage
D. They disable hallucinations

Answer

A. They simplify automation and system integration

Explanation

Structured outputs are easier for applications and APIs to process programmatically.

Question 4

What is a hallucination in generative AI?

A. A valid JSON schema
B. Unsupported or invented model output
C. A GPU optimization technique
D. An OCR extraction method

Answer

B. Unsupported or invented model output

Explanation

Hallucinations occur when models generate incorrect or fabricated information.

Question 5

What is few-shot prompting?

A. Disabling prompts entirely
B. Compressing token usage automatically
C. Providing examples within prompts to guide model behavior
D. Encrypting prompt flows

Answer

C. Providing examples within prompts to guide model behavior

Explanation

Few-shot prompting improves output quality by demonstrating desired behavior.

Question 6

Which Azure service supports prompt flow orchestration?

A. Azure AI Foundry
B. Azure DNS
C. Azure Firewall
D. Azure CDN

Answer

A. Azure AI Foundry

Explanation

Azure AI Foundry supports prompt flows, orchestration, and AI workflow management.

Question 7

What is Retrieval-Augmented Generation (RAG)?

A. Combining retrieval systems with generative AI for grounded responses
B. Compressing OCR results
C. Encrypting vector embeddings
D. Removing JSON outputs

Answer

A. Combining retrieval systems with generative AI for grounded responses

Explanation

RAG retrieves relevant information before generating responses.

Question 8

Why should generated JSON outputs be validated?

A. To disable summarization
B. To reduce OCR latency
C. To ensure schema correctness and prevent parsing failures
D. To eliminate vector search

Answer

C. To ensure schema correctness and prevent parsing failures

Explanation

Validation ensures outputs are properly structured and usable downstream.

Question 9

Which Azure service supports generative summarization and entity extraction?

A. Azure Virtual WAN
B. Azure ExpressRoute
C. Azure Firewall
D. Azure OpenAI Service

Answer

D. Azure OpenAI Service

Explanation

Azure OpenAI Service supports generative AI-based text analysis workflows.

Question 10

What is a best practice for reducing hallucinations?

A. Disable monitoring systems
B. Automatically trust all outputs
C. Use grounded prompts and validation workflows
D. Avoid structured outputs

Answer

C. Use grounded prompts and validation workflows

Explanation

Grounding and validation help reduce unsupported or fabricated outputs.

Go to the AI-103 Exam Prep Hub main page

AI, AI-901, azure, Microsoft Certification

Build a lightweight application with Information Extraction capabilities by using Content Understanding (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:
Implement AI solutions by using Microsoft Foundry (55–60%)
   --> Implement AI solutions for information extraction by using Foundry
      --> Build a lightweight application with Information Extraction capabilities by using Content Understanding

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.

Modern organizations often need applications that can automatically extract information from documents, images, audio, and video. Azure AI services and Microsoft Foundry tools make it possible to create lightweight applications that use AI-powered content understanding without requiring advanced machine learning expertise.

For the AI-901 certification exam, candidates should understand the foundational concepts involved in building lightweight applications with information extraction capabilities by using Azure Content Understanding and Microsoft Foundry.

This topic falls under the “Implement AI solutions for information extraction by using Foundry” section of the AI-901 exam objectives.

What Is Information Extraction?

Information extraction is the process of automatically identifying and retrieving useful data from content.

AI systems can extract information from:

  • Documents
  • Images
  • Audio
  • Video
  • Text

Examples include:

  • Names
  • Dates
  • Invoice totals
  • Keywords
  • Objects
  • Spoken words

What Is Azure Content Understanding?

Azure Content Understanding enables AI-powered analysis of different types of content.

Capabilities include:

  • OCR (Optical Character Recognition)
  • Speech recognition
  • Entity extraction
  • Image analysis
  • Video analysis
  • Classification
  • Caption generation

What Is a Lightweight Application?

A lightweight application is a simple application that performs focused tasks using cloud-based AI services.

Characteristics include:

  • Minimal infrastructure
  • API-based communication
  • Rapid development
  • Simple user interface
  • Cloud-hosted AI processing

For AI-901, candidates should understand concepts and workflows rather than advanced coding details.

Azure AI Foundry

Azure AI Foundry provides tools for building and testing AI applications.

Developers can:

  • Access AI models
  • Configure services
  • Test prompts
  • Analyze content
  • Build AI-powered workflows

Common Information Extraction Capabilities

OCR (Optical Character Recognition)

OCR extracts text from images and scanned documents.

Example

Input

Photo of a receipt

Output

  • Store name
  • Total amount
  • Purchase date

Entity Extraction

AI systems can identify important entities within content.

Examples of Entities

  • Names
  • Locations
  • Organizations
  • Phone numbers
  • Dates

Speech Recognition

Speech recognition converts spoken language into text.

Example

Input

Customer support call recording

Output

Searchable transcript

Object Detection

Object detection identifies objects within images or video.

Example

A warehouse-monitoring application may detect:

  • Boxes
  • Forklifts
  • Employees

Sentiment Analysis

Sentiment analysis determines emotional tone.

Example

Customer feedback classified as:

  • Positive
  • Neutral
  • Negative

Typical Lightweight Application Workflow

A lightweight information-extraction application often follows these steps:

  1. User uploads content
  2. Application sends content to Azure AI service
  3. AI analyzes content
  4. Structured results are returned
  5. Application displays extracted information

Example Workflow

User uploads:

  • Image
  • PDF
  • Audio file
  • Video file

AI extracts:

  • Text
  • Keywords
  • Objects
  • Entities
  • Captions

APIs and Endpoints

Applications communicate with Azure AI services through:

  • APIs
  • Endpoints

The application sends content to the AI service and receives structured results.

Authentication

Applications must authenticate securely before using Azure AI services.

Common authentication methods include:

  • API keys
  • Azure credentials
  • Managed identities

Example High-Level Pseudocode

content = upload_file()
results = analyze_content(content)
display_results(results)

For AI-901, understanding the workflow is more important than memorizing exact syntax.

Structured Outputs

AI systems often return structured data formats such as:

  • JSON
  • Tables
  • Lists
  • Metadata

Structured outputs make integration easier.

Example JSON-Like Output

{
  "invoiceNumber": "INV-1001",
  "date": "2026-05-15",
  "total": "$245.99"
}

Common Real-World Scenarios

Scenario 1: Invoice Processing

Goal

Automatically extract invoice data.

Extracted Information

  • Vendor name
  • Invoice number
  • Total amount
  • Due date

Scenario 2: Customer Service Analytics

Goal

Analyze customer interactions.

Extracted Information

  • Topics
  • Sentiment
  • Keywords
  • Transcripts

Scenario 3: Healthcare Document Analysis

Goal

Extract information from medical documents.

Extracted Information

  • Patient names
  • Dates
  • Medical terms

Scenario 4: Media Monitoring

Goal

Analyze audio and video content.

Extracted Information

  • Captions
  • Objects
  • Speakers
  • Keywords

Responsible AI Considerations

Information-extraction applications should follow Responsible AI principles.

Key considerations include:

  • Privacy
  • Fairness
  • Transparency
  • Inclusiveness
  • Accountability
  • Security

Privacy Concerns

Content may contain:

  • Personal information
  • Financial records
  • Medical data
  • Private conversations

Organizations should secure sensitive data appropriately.

Fairness and Bias

AI systems may perform differently across:

  • Languages
  • Accents
  • Demographics
  • Image quality
  • Environmental conditions

Testing and evaluation are important.

Transparency

Users should understand:

  • AI is analyzing their content
  • AI-generated outputs may contain errors
  • Human review may still be needed

Accuracy Limitations

Information-extraction systems may struggle with:

  • Blurry images
  • Poor audio quality
  • Handwritten text
  • Background noise
  • Low-resolution files

Hallucinations and Errors

AI systems may occasionally:

  • Extract incorrect information
  • Misidentify objects
  • Misinterpret speech
  • Generate inaccurate summaries

Applications should validate important outputs.

Error Handling

Applications should handle:

  • Unsupported file formats
  • Corrupted files
  • Authentication failures
  • Network interruptions
  • Rate limits

Advantages of Lightweight AI Applications

Benefits include:

  • Rapid deployment
  • Reduced development complexity
  • Scalability
  • Automation
  • Faster information processing

Limitations of Lightweight AI Applications

Challenges include:

  • Dependence on cloud services
  • Accuracy limitations
  • Privacy concerns
  • Potential bias
  • Environmental variability

Multimodal AI

Modern AI systems can combine:

  • Text
  • Speech
  • Vision
  • Generative AI

These systems can process multiple content types together.

High-Level Architecture

A simplified architecture often includes:

  1. User uploads content
  2. Application sends content to Azure AI service
  3. AI analyzes content
  4. Structured results are returned
  5. Application displays extracted information

Important AI-901 Exam Tips

For the exam, remember these key points:

  • Information extraction retrieves useful data from content.
  • OCR extracts text from images and documents.
  • Speech recognition converts speech into text.
  • Object detection identifies objects within images or video.
  • APIs and endpoints connect applications to Azure AI services.
  • Authentication secures access to AI resources.
  • Structured outputs often use JSON-like formats.
  • Responsible AI principles apply to information extraction systems.
  • Poor-quality content can reduce accuracy.
  • Hallucinations are inaccurate AI-generated outputs.
  • Azure AI Foundry supports AI application development.

Quick Knowledge Check

Question 1

What does OCR do?

Answer

Extracts text from images and scanned documents.

Question 2

What does speech recognition do?

Answer

Converts spoken language into text.

Question 3

Why is authentication important?

Answer

It secures access to Azure AI services.

Question 4

What can reduce information-extraction accuracy?

Answer

Poor-quality images, background noise, and blurry documents.

Practice Exam Questions

Exam: AI-901

Topic: Build a Lightweight Application with Information Extraction Capabilities by Using Content Understanding

Question 1

What is the PRIMARY purpose of information extraction in AI applications?

A. To automatically retrieve useful data from content
B. To increase internet speed
C. To replace operating systems
D. To improve monitor resolution

Correct Answer

A. To automatically retrieve useful data from content

Explanation

Information extraction uses AI to identify and retrieve meaningful data from documents, images, audio, video, and text.

Why the Other Answers Are Incorrect

B. To increase internet speed

Information extraction does not improve networking performance.

C. To replace operating systems

AI extraction tools do not replace operating systems.

D. To improve monitor resolution

This is unrelated to AI information extraction.

Question 2

What does OCR stand for?

A. Optical Character Recognition
B. Open Cloud Routing
C. Operational Content Reporting
D. Object Classification Retrieval

Correct Answer

A. Optical Character Recognition

Explanation

OCR extracts machine-readable text from images and scanned documents.

Why the Other Answers Are Incorrect

B. Open Cloud Routing

This is not an OCR term.

C. Operational Content Reporting

This is unrelated to text extraction.

D. Object Classification Retrieval

This is not the meaning of OCR.

Question 3

Which AI capability converts spoken language into text?

A. Speech recognition
B. Image classification
C. Speech synthesis
D. Object detection

Correct Answer

A. Speech recognition

Explanation

Speech recognition transcribes spoken words into text.

Why the Other Answers Are Incorrect

B. Image classification

This categorizes images.

C. Speech synthesis

This converts text into spoken audio.

D. Object detection

This identifies objects within images or video.

Question 4

What is a lightweight AI application?

A. A simple application that uses cloud AI services for focused tasks
B. A hardware-only system
C. A networking device
D. A spreadsheet management tool

Correct Answer

A. A simple application that uses cloud AI services for focused tasks

Explanation

Lightweight applications typically use APIs and cloud services to provide AI capabilities without requiring complex infrastructure.

Why the Other Answers Are Incorrect

B. A hardware-only system

Lightweight AI apps commonly use cloud services.

C. A networking device

Networking devices are unrelated.

D. A spreadsheet management tool

This is unrelated to AI application design.

Question 5

How do lightweight AI applications commonly communicate with Azure AI services?

A. Through APIs and endpoints
B. Through printer drivers
C. Through monitor settings
D. Through USB-only connections

Correct Answer

A. Through APIs and endpoints

Explanation

Applications use APIs and endpoints to send content to Azure AI services and receive analysis results.

Why the Other Answers Are Incorrect

B. Through printer drivers

Printers are unrelated to Azure AI communication.

C. Through monitor settings

This is unrelated to cloud AI services.

D. Through USB-only connections

Cloud AI services use network communication.

Question 6

Why is authentication important in Azure AI applications?

A. To secure access to AI resources
B. To improve image brightness
C. To increase network speed
D. To improve speaker volume

Correct Answer

A. To secure access to AI resources

Explanation

Authentication ensures that only authorized users and applications can access Azure AI services.

Why the Other Answers Are Incorrect

B. To improve image brightness

Authentication does not affect image quality.

C. To increase network speed

Authentication does not improve networking.

D. To improve speaker volume

Authentication does not affect audio playback.

Question 7

Which format is commonly used for structured AI output data?

A. JSON
B. JPEG
C. MP3
D. ZIP

Correct Answer

A. JSON

Explanation

AI systems often return structured data in JSON-like formats for easy application integration.

Why the Other Answers Are Incorrect

B. JPEG

JPEG is an image format.

C. MP3

MP3 is an audio format.

D. ZIP

ZIP is a compressed archive format.

Question 8

Which factor can reduce information-extraction accuracy?

A. Poor-quality input content
B. Spreadsheet formatting
C. Keyboard layout changes
D. Screen brightness settings

Correct Answer

A. Poor-quality input content

Explanation

Blurry images, poor audio quality, and noisy environments can negatively affect AI extraction accuracy.

Why the Other Answers Are Incorrect

B. Spreadsheet formatting

This does not affect AI extraction services.

C. Keyboard layout changes

This is unrelated to AI analysis.

D. Screen brightness settings

This does not affect AI processing accuracy.

Question 9

Which Responsible AI concern is especially important for information extraction applications?

A. Protecting sensitive personal data
B. Increasing printer performance
C. Improving spreadsheet formulas
D. Reducing monitor power usage

Correct Answer

A. Protecting sensitive personal data

Explanation

Extracted content may contain financial, medical, or personal information that must be protected securely.

Why the Other Answers Are Incorrect

B. Increasing printer performance

This is unrelated to Responsible AI.

C. Improving spreadsheet formulas

This is unrelated to information extraction.

D. Reducing monitor power usage

This is unrelated to AI ethics.

Question 10

What are hallucinations in AI information-extraction systems?

A. Incorrect or fabricated AI-generated outputs
B. Hardware installation failures
C. Network outages
D. Operating system crashes

Correct Answer

A. Incorrect or fabricated AI-generated outputs

Explanation

Hallucinations occur when AI systems generate inaccurate extracted information, captions, summaries, or identifications.

Why the Other Answers Are Incorrect

B. Hardware installation failures

This is unrelated to AI-generated outputs.

C. Network outages

This is a connectivity issue.

D. Operating system crashes

This is unrelated to AI hallucinations.

Final Thoughts

Building lightweight applications with information extraction capabilities is an important topic for the AI-901 certification exam. Microsoft expects candidates to understand foundational concepts such as OCR, speech recognition, APIs, authentication, structured outputs, Responsible AI principles, and lightweight AI workflows.

Azure AI services and Azure AI Foundry provide powerful tools for creating scalable applications capable of extracting valuable information from text, images, audio, video, and documents.

Go to the AI-901 Exam Prep Hub main page