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
      --> Extract information from documents and forms by using Azure Content Understanding in Foundry Tools

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.

Organizations process enormous amounts of documents every day, including invoices, receipts, forms, contracts, and identification documents. AI-powered information extraction solutions help automate the process of reading, understanding, and organizing document data.

For the AI-901 certification exam, candidates should understand the foundational concepts behind extracting information from documents and forms by using Azure Content Understanding and Microsoft Foundry tools.

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 identifying and retrieving useful data from documents, images, forms, audio, or other content.

Examples include extracting:

Names
Dates
Invoice totals
Addresses
Phone numbers
Product information

What Is Azure Content Understanding?

Azure Content Understanding helps AI systems analyze and interpret structured and unstructured documents.

Capabilities include:

Text extraction
Form recognition
Document analysis
Information classification
Key-value pair extraction

Azure AI Foundry

Azure AI Foundry provides tools for building, testing, and managing AI-powered applications.

Developers can:

Configure AI services
Process documents
Test extraction workflows
Build lightweight AI applications

Structured vs. Unstructured Documents

Structured Documents

Structured documents follow a consistent layout.

Examples include:

Tax forms
Invoices
Receipts
Application forms

Unstructured Documents

Unstructured documents have less predictable layouts.

Examples include:

Emails
Letters
Articles
Contracts

Optical Character Recognition (OCR)

OCR converts text within images or scanned documents into machine-readable text.

Example

Input

Scanned receipt image

OCR Output

Store name
Date
Total amount

Form Recognition

Form recognition identifies fields and values within forms.

Example

Form

Insurance application

Extracted Data

Customer name
Policy number
Address
Claim amount

Key-Value Pair Extraction

AI systems can identify relationships between labels and values.

Example

Key	Value
Invoice Number	INV-1045
Total	$250.00
Due Date	05/30/2026

Table Extraction

AI can identify and extract tables from documents.

Example

A receipt table may contain:

Item names
Quantities
Prices

Classification

Document classification identifies the type of document being processed.

Example

The system determines whether a file is:

Invoice
Contract
Receipt
Resume

Named Entity Recognition (NER)

NER identifies important entities within text.

Entities may include:

People
Organizations
Locations
Dates

Example

Text

“John Smith works for Contoso in Seattle.”

Extracted Entities

John Smith (Person)
Contoso (Organization)
Seattle (Location)

APIs and Endpoints

Applications communicate with Azure AI services through:

APIs
Endpoints

Documents are submitted for analysis programmatically.

Authentication

Applications must securely authenticate before accessing Azure AI services.

Common authentication methods include:

API keys
Azure credentials
Managed identities

Lightweight Application Workflow

A typical workflow includes:

User uploads document
Application sends file to AI service
AI extracts information
Results are returned
Application displays or stores extracted data

Example Workflow

Input

Scanned invoice

AI Processing

OCR
Key-value extraction
Table analysis

Output

Structured invoice data

Example High-Level Pseudocode

			
document = upload_document()
results = analyze_document(document)
display_results(results)

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

Common Real-World Scenarios

Scenario 1: Invoice Processing

Goal

Automate invoice data extraction.

Features

OCR
Table extraction
Total amount detection

Scenario 2: Receipt Scanning

Goal

Extract purchase information from receipts.

Features

Text extraction
Merchant identification
Expense categorization

Scenario 3: Resume Processing

Goal

Extract candidate information from resumes.

Features

Name extraction
Skill identification
Contact information detection

Scenario 4: Healthcare Forms

Goal

Digitize patient records.

Features

Form recognition
Key-value extraction
Classification

Responsible AI Considerations

Document-processing applications should follow Responsible AI principles.

Key considerations include:

Privacy
Security
Fairness
Transparency
Accountability
Inclusiveness

Privacy Concerns

Documents may contain:

Personal information
Financial data
Medical information
Legal records

Organizations should protect sensitive data appropriately.

Security Considerations

Applications should secure:

Uploaded files
Stored documents
API credentials
Extracted data

Transparency

Users should understand:

AI is analyzing documents
Extracted data may contain errors
Human review may still be needed

Accuracy Limitations

AI extraction systems may struggle with:

Poor scan quality
Handwritten text
Complex layouts
Damaged documents

Hallucinations and Errors

AI systems may occasionally:

Extract incorrect values
Miss fields
Misclassify documents

Applications should validate important information.

Error Handling

Applications should handle:

Unsupported file formats
Corrupted documents
Authentication failures
Network interruptions
Rate limits

Advantages of Information Extraction AI

Benefits include:

Faster document processing
Reduced manual entry
Improved scalability
Increased automation
Better searchability

Limitations of Information Extraction AI

Challenges include:

Variable document quality
Handwriting recognition difficulties
Inconsistent layouts
Privacy concerns
Extraction inaccuracies

Generative AI and Information Extraction

Some modern systems combine:

OCR
Document intelligence
Generative AI

This enables:

Summarization
Question answering
Conversational document analysis

High-Level Architecture

A simplified architecture often includes:

User uploads document
Application sends document to Azure AI service
AI analyzes content
Structured data is returned
Application displays or stores results

Important AI-901 Exam Tips

For the exam, remember these key points:

OCR extracts text from documents and images.
Form recognition identifies fields and values.
Key-value extraction identifies label-value relationships.
Table extraction retrieves structured table data.
Classification identifies document types.
APIs and endpoints connect applications to Azure AI services.
Authentication secures access to AI resources.
Responsible AI principles apply to document-processing systems.
Poor document quality can reduce extraction accuracy.
AI-generated outputs may still require validation.

Quick Knowledge Check

Question 1

What does OCR do?

Answer

Extracts machine-readable text from images or scanned documents.

Question 2

What is form recognition?

Answer

Identifying and extracting fields and values from forms.

Question 3

Why is authentication important?

Answer

It secures access to Azure AI services and protects resources.

Question 4

What can reduce extraction accuracy?

Answer

Poor scan quality, handwriting, and inconsistent document layouts.

Practice Exam Questions

Exam: AI-901

Topic: Extract Information from Documents and Forms by Using Azure Content Understanding in Foundry Tools

Question 1

What is the PRIMARY purpose of information extraction AI solutions?

A. To retrieve useful data from documents and content
B. To increase internet bandwidth
C. To replace operating systems
D. To improve monitor resolution

Correct Answer

A. To retrieve useful data from documents and content

Explanation

Information extraction AI systems identify and retrieve meaningful information such as names, dates, totals, and addresses from documents and forms.

Why the Other Answers Are Incorrect

B. To increase internet bandwidth

Information extraction does not affect network speed.

C. To replace operating systems

AI document processing does 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 Content Retrieval
C. Object Classification Routing
D. Operational Compute Reporting

Correct Answer

A. Optical Character Recognition

Explanation

OCR converts printed or handwritten text within images and scanned documents into machine-readable text.

Why the Other Answers Are Incorrect

B. Open Content Retrieval

This is not the meaning of OCR.

C. Object Classification Routing

This is unrelated to document analysis.

D. Operational Compute Reporting

This is not an OCR term.

Question 3

Which AI capability identifies fields and values within forms?

A. Form recognition
B. Speech synthesis
C. Image compression
D. Network monitoring

Correct Answer

A. Form recognition

Explanation

Form recognition extracts structured information such as names, dates, totals, and addresses from forms and documents.

Why the Other Answers Are Incorrect

B. Speech synthesis

This converts text into speech.

C. Image compression

This reduces file size and is unrelated to field extraction.

D. Network monitoring

This is unrelated to document AI.

Question 4

Which Azure platform provides tools for building and managing AI-powered applications?

A. Azure AI Foundry
B. Microsoft Paint
C. Windows Task Manager
D. Azure DNS

Correct Answer

A. Azure AI Foundry

Explanation

Azure AI Foundry provides tools for deploying, testing, and managing AI applications and services.

Why the Other Answers Are Incorrect

B. Microsoft Paint

Paint is a graphics editor.

C. Windows Task Manager

This is a system monitoring tool.

D. Azure DNS

This is a networking service.

Question 5

What is key-value pair extraction?

A. Identifying labels and their associated values in documents
B. Encrypting document files
C. Compressing image sizes
D. Converting audio into text

Correct Answer

A. Identifying labels and their associated values in documents

Explanation

Key-value extraction identifies relationships such as:

Invoice Number → INV-1045
Total → $250.00

Why the Other Answers Are Incorrect

B. Encrypting document files

Encryption is unrelated to data extraction.

C. Compressing image sizes

Compression is unrelated to document intelligence.

D. Converting audio into text

This is speech recognition.

Question 6

What is the purpose of document classification?

A. To identify the type of document being processed
B. To increase network performance
C. To generate music files
D. To repair damaged documents physically

Correct Answer

A. To identify the type of document being processed

Explanation

Document classification determines whether a file is an invoice, contract, receipt, resume, or another document type.

Why the Other Answers Are Incorrect

B. To increase network performance

Classification does not improve networking.

C. To generate music files

This is unrelated to document AI.

D. To repair damaged documents physically

AI classification does not physically repair documents.

Question 7

How do lightweight document-processing applications typically communicate with Azure AI services?

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

Correct Answer

A. Through APIs and endpoints

Explanation

Applications send documents to Azure AI services using APIs and endpoints and receive structured analysis results.

Why the Other Answers Are Incorrect

B. Through USB-only connections

Cloud services use network communication.

C. Through monitor calibration tools

This is unrelated to AI services.

D. Through printer drivers

Printers are unrelated to cloud AI communication.

Question 8

Which factor can reduce the accuracy of document extraction systems?

A. Poor document quality
B. Spreadsheet color themes
C. Keyboard layout changes
D. Audio playback speed

Correct Answer

A. Poor document quality

Explanation

Blurry scans, damaged pages, handwriting, and poor lighting can negatively affect extraction accuracy.

Why the Other Answers Are Incorrect

B. Spreadsheet color themes

This does not affect document extraction AI.

C. Keyboard layout changes

This is unrelated to AI document analysis.

D. Audio playback speed

This is unrelated to document processing.

Question 9

Why is authentication important when using Azure AI services?

A. To secure access to AI resources
B. To improve image resolution
C. To increase internet speed
D. To compress document files

Correct Answer

A. To secure access to AI resources

Explanation

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

Why the Other Answers Are Incorrect

B. To improve image resolution

Authentication does not affect image quality.

C. To increase internet speed

Authentication does not improve networking.

D. To compress document files

Authentication is unrelated to file compression.

Question 10

Which Responsible AI concern is especially important when processing documents?

A. Protecting sensitive personal information
B. Increasing monitor brightness
C. Improving printer speed
D. Reducing spreadsheet file size

Correct Answer

A. Protecting sensitive personal information

Explanation

Documents may contain financial, medical, legal, or personal information that must be protected appropriately.

Why the Other Answers Are Incorrect

B. Increasing monitor brightness

This is unrelated to Responsible AI.

C. Improving printer speed

This is unrelated to document intelligence.

D. Reducing spreadsheet file size

This is unrelated to AI ethics or privacy.

Final Thoughts

Extracting information from documents and forms using Azure Content Understanding and Foundry tools is an important topic for the AI-901 certification exam. Microsoft expects candidates to understand foundational concepts such as OCR, form recognition, document analysis, APIs, authentication, Responsible AI principles, and lightweight document-processing workflows.

Azure AI services and Azure AI Foundry provide powerful tools for automating information extraction and improving efficiency across business, healthcare, finance, and administrative scenarios.

Go to the AI-901 Exam Prep Hub main page