This post is a part of the AB-620: Designing and Building Integrated AI Agent Solutions in Copilot Studio Exam Prep Hub.
This topic falls under these sections:
Plan and configure agent solutions (30–35%)
   --> Configure topics
      --> Configure advanced agent responses with custom knowledge sources

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Introduction

One of the greatest strengths of Microsoft Copilot Studio is the ability to ground AI-generated responses using enterprise knowledge instead of relying solely on the large language model’s general knowledge. This capability allows organizations to build intelligent agents that answer questions using trusted business information while reducing hallucinations and ensuring responses remain accurate, current, and relevant.

For the AB-620 certification exam, you should understand not only how to configure custom knowledge sources, but also when each type should be used, their limitations, security implications, and how they fit into an enterprise AI architecture.

This topic frequently overlaps with several other exam objectives, including:

• Designing enterprise integration strategies
• Grounding agents with enterprise knowledge
• Configuring generative answers
• Implementing governance and Responsible AI
• Designing reusable agent components

What Are Custom Knowledge Sources?

A custom knowledge source is any repository of information that an agent can use to answer user questions.

Instead of answering solely from the language model’s pre-trained knowledge, the agent retrieves relevant enterprise content and uses it to formulate a grounded response.

This process is commonly called Retrieval-Augmented Generation (RAG).

Instead of:

User Question → Large Language Model → Response

the process becomes:

User Question → Search Enterprise Knowledge → Retrieve Relevant Content → Large Language Model Generates Grounded Response

This dramatically improves:

• Accuracy
• Trustworthiness
• Freshness
• Business relevance
• Compliance

Why Ground Responses?

Without grounding, AI may:

• invent information
• provide outdated answers
• misunderstand company terminology
• answer questions outside company policy

Grounding ensures responses come from trusted organizational content.

Example:

Without grounding:

“Our PTO policy allows 25 vacation days.”

Grounded response:

“According to the HR handbook updated in January 2026, employees receive 15 days during years 1–5.”

Benefits of Custom Knowledge Sources

Organizations use knowledge grounding to:

• Answer employee questions
• Answer customer questions
• Reduce support tickets
• Provide product documentation
• Deliver policy guidance
• Search internal knowledge bases
• Surface procedures
• Retrieve manuals
• Access compliance documents
• Improve consistency

Typical Enterprise Scenarios

Human Resources

Employees ask:

• How many vacation days do I receive?
• What is parental leave?
• How do I submit expenses?

The agent searches:

• HR SharePoint
• Employee handbook
• Policy documents

IT Help Desk

Users ask:

• Reset password
• VPN setup
• Install software
• MFA issues

Knowledge sources include:

• IT documentation
• Knowledge base
• Support articles

Customer Service

Customers ask:

• Product specifications
• Warranty information
• Pricing
• Returns

Knowledge sources:

• Product manuals
• FAQ databases
• Documentation
• CRM articles

Healthcare

Agents answer questions using:

• Clinical procedures
• Patient documentation
• Internal policies
• Approved treatment guidelines

Manufacturing

Knowledge sources include:

• Equipment manuals
• Safety procedures
• Maintenance documentation
• Production SOPs

Types of Knowledge Sources

Copilot Studio supports multiple knowledge sources.

Understanding which one fits each scenario is important for the exam.

SharePoint

One of the most common enterprise sources.

Ideal for:

• Policies
• Procedures
• Manuals
• Internal documentation

Advantages:

• Already used by many organizations
• Supports permissions
• Frequently updated
• Easy integration

Example:

Employee:

“What is our travel reimbursement policy?”

Agent retrieves:

TravelPolicy.docx stored in SharePoint.

Public Websites

Useful for:

• Public documentation
• FAQs
• Knowledge portals
• Product documentation

Example:

A software company exposes:

support.contoso.com

The agent retrieves relevant pages.

Advantages:

• Always current
• Easy to maintain
• No document uploads

Uploaded Files

Supported document types include examples such as:

• PDF
• DOCX
• PPTX
• TXT

Useful for:

• Training manuals
• Internal guides
• Product documentation

Best for:

Small knowledge collections.

Dataverse

Dataverse can act as structured enterprise knowledge.

Useful for:

• Business records
• Customer information
• Products
• Inventory
• Services

Unlike documents, Dataverse contains structured tables.

Example:

Instead of searching a PDF catalog:

The agent queries a Products table.

Azure AI Search

Azure AI Search is Microsoft’s enterprise search platform.

It is ideal for:

• Millions of documents
• Large organizations
• Multiple repositories
• Advanced indexing
• Semantic search
• Hybrid search
• Vector search

Azure AI Search is commonly used when enterprise knowledge becomes too large for basic document collections.

External Connectors

Organizations often store information outside Microsoft 365.

Examples include:

• Salesforce
• ServiceNow
• Confluence
• Zendesk
• SAP
• Oracle systems

Connectors allow agents to retrieve information from these systems.

Microsoft Graph

Microsoft Graph provides access to Microsoft 365 resources.

Examples include:

• Outlook
• Teams
• OneDrive
• SharePoint
• Calendar

Agents can retrieve user-specific information when appropriate permissions are granted.

Structured vs. Unstructured Knowledge

Understanding this distinction is important.

Structured Knowledge

Examples:

• Dataverse
• SQL
• CRM records
• ERP systems

Characteristics:

• Tables
• Rows
• Columns
• Predictable schema

Best for:

Business data.

Unstructured Knowledge

Examples:

• PDFs
• Word documents
• Policies
• Web pages
• Wikis

Characteristics:

• Natural language
• Paragraphs
• Articles
• Documentation

Best for:

Generative answers.

Choosing the Right Knowledge Source

Custom Knowledge Sources vs. Custom Prompts

This distinction is frequently tested.

Custom Prompts

Control:

• Writing style
• Tone
• Personality
• Formatting
• Instructions

Examples:

• “Answer formally.”
• “Always summarize first.”
• “Respond in bullet points.”

Prompts influence how the AI answers.

Custom Knowledge

Controls:

• Facts
• Information
• Source material
• Evidence

Examples:

• HR handbook
• Product manual
• Company policy

Knowledge determines what the AI answers.

Together

A high-quality enterprise agent uses both.

Custom Prompt:

Respond professionally using short paragraphs.

Custom Knowledge:

HR Policy Handbook

The prompt determines presentation.

The knowledge determines accuracy.

Knowledge Grounding Process

A typical request follows these steps:

Step 1

User submits a question.

↓

Step 2

Copilot determines whether enterprise knowledge is needed.

↓

Step 3

Searches configured knowledge sources.

↓

Step 4

Ranks relevant documents.

↓

Step 5

Retrieves the most relevant passages.

↓

Step 6

Uses the retrieved content as context.

↓

Step 7

LLM generates the final grounded response.

Designing Enterprise Knowledge Architecture

Successful enterprise deployments rarely rely on a single repository.

Instead, organizations often build layered knowledge architectures.

Example:

Layer 1

Public website

↓

Layer 2

SharePoint documentation

↓

Layer 3

Azure AI Search index

↓

Layer 4

Dataverse

↓

Layer 5

External business systems

This allows agents to answer increasingly sophisticated questions while using the most appropriate source.

Best Practices

Keep Knowledge Current

Outdated documentation leads to outdated answers.

Review knowledge regularly.

Remove Duplicate Documents

Multiple conflicting versions reduce answer quality.

Maintain a single authoritative version whenever possible.

Organize Content Logically

Use:

• Clear folder structures
• Consistent naming
• Well-defined document ownership

Good organization improves retrieval quality.

Use Smaller, Focused Documents

Instead of one 300-page manual:

Use multiple focused documents.

Benefits include:

• Better retrieval
• More relevant passages
• Higher response quality

Write Clearly

Documents should use:

• Plain language
• Headings
• Lists
• Consistent terminology

Well-written content produces better AI answers.

Apply Security

Only expose information users should access.

Respect existing permissions.

Never use AI to bypass organizational security.

Common Exam Pitfalls

Candidates often confuse:

• Custom prompts with custom knowledge
• Knowledge grounding with connector actions
• Dataverse with document repositories
• Azure AI Search with SharePoint
• Enterprise search with generative responses

Remember:

• Custom prompts shape the response.
• Custom knowledge sources provide factual grounding.
• Connectors retrieve or update operational data.
• Azure AI Search is optimized for enterprise-scale search.
• Dataverse stores structured business information.

Exam Tips

For the AB-620 exam, be prepared to:

• Differentiate structured and unstructured knowledge sources.
• Select the most appropriate knowledge source for a given business scenario.
• Explain how retrieval-augmented generation (RAG) improves response quality.
• Compare SharePoint, Dataverse, Azure AI Search, websites, uploaded documents, and external connectors.
• Recognize when Azure AI Search is preferable to standard document collections.
• Distinguish between custom prompts and custom knowledge sources.
• Design scalable, secure knowledge architectures that support enterprise AI agents.
• Identify best practices for maintaining high-quality, trustworthy knowledge repositories.

Quick Orientation Summary

In the topics above, you learned about the purpose of custom knowledge sources, supported knowledge repositories, retrieval-augmented generation (RAG), and best practices for designing enterprise knowledge architectures.

In the topics below, we will focus on advanced implementation considerations, security and governance, optimization strategies, troubleshooting, and conclude with ten practice exam questions.

Advanced Knowledge Grounding Strategies

Enterprise AI agents often need to search multiple repositories simultaneously. Rather than relying on a single knowledge source, organizations typically combine several repositories to maximize answer quality.

Example architecture:

• Public product documentation
• Internal SharePoint sites
• Azure AI Search indexes
• Dataverse tables
• External knowledge bases
• Microsoft Graph resources

When a user asks a question, Copilot Studio determines which configured sources are relevant, retrieves supporting information, and uses the language model to generate a grounded response.

This layered approach provides:

• Higher answer accuracy
• Broader organizational coverage
• Better scalability
• Easier maintenance
• Reduced hallucinations

Selecting the Appropriate Knowledge Source

One of the most common AB-620 exam scenarios asks which knowledge source should be used.

Choose SharePoint when:

• Company documentation already exists
• Policies change regularly
• Permissions must follow Microsoft 365 security
• Knowledge is primarily document-based

Choose Azure AI Search when:

• Millions of documents exist
• Multiple repositories must be searched
• Semantic search is required
• Vector search improves relevance
• Enterprise-scale performance is needed

Choose Dataverse when:

• Information is highly structured
• Records change frequently
• Business applications already use Dataverse
• Data relationships are important

Choose Website Knowledge when:

• Information is publicly available
• Documentation is maintained online
• Customers require self-service support
• No authentication is required

Choose Uploaded Documents when:

• Small knowledge collections exist
• Pilot projects are being developed
• Documentation is static
• Quick deployment is desired

Security Considerations

Security is a significant exam objective because AI should never expose information users are not authorized to access.

A well-designed agent should respect existing security controls instead of bypassing them.

Key principles include:

• Least privilege access
• Identity-aware authentication
• Permission inheritance
• Secure connector configuration
• Protected credentials
• Secure storage of secrets

Authentication

Knowledge sources often require authentication.

Examples include:

• Microsoft Entra ID
• OAuth
• API Keys
• Managed Identity
• Service Principals

Authentication ensures only authorized users and applications can retrieve enterprise information.

Authorization

Authentication answers:

Who are you?

Authorization answers:

What are you allowed to access?

For example:

Employee A may access:

• HR policies
• Employee handbook

Employee B (HR Manager) may additionally access:

• Salary guidelines
• Benefits administration documents

The agent should return only information the current user is authorized to view.

Governance Considerations

Governance ensures AI solutions remain secure, compliant, and manageable over time.

Important governance practices include:

• Content lifecycle management
• Document ownership
• Data classification
• Information retention
• Audit logging
• Compliance monitoring

Organizations should regularly review knowledge repositories to remove outdated or conflicting information.

Maintaining Knowledge Quality

AI quality depends heavily on knowledge quality.

Poor documentation results in poor answers.

High-quality knowledge repositories should be:

• Current
• Accurate
• Complete
• Well-organized
• Clearly written
• Free of duplicate information

Good Example

Policy:

Employees receive 15 vacation days during their first five years of employment.

Simple, clear, and easy to retrieve.

Poor Example

Vacation...
Unless otherwise specified...
depending on previous agreements...
except...
refer to Appendix D...

Long, ambiguous documents reduce retrieval quality.

Optimizing Documents for AI

Large language models perform better when documents are structured logically.

Recommendations include:

Use headings

Instead of:

Large blocks of text

Use:

• Overview
• Eligibility
• Procedures
• Exceptions
• Contacts

Use Lists

Lists improve retrieval.

Example:

Expense reimbursement includes:

• Hotel
• Airfare
• Mileage
• Parking

instead of long paragraphs.

Break Large Documents Apart

Instead of:

EmployeeHandbook_900Pages.pdf

Use:

• Vacation Policy
• Sick Leave
• Benefits
• Travel Policy
• Remote Work Policy

Smaller documents improve retrieval precision.

Azure AI Search Considerations

Azure AI Search offers advanced enterprise capabilities beyond simple document search.

Important concepts include:

Semantic Search

Understands meaning instead of matching keywords.

Example:

Question:

“How many vacation days?”

Matches:

Paid Time Off Policy

even if the phrase “vacation days” never appears.

Vector Search

Uses embeddings to locate conceptually similar information.

Useful for:

• Similar questions
• Natural language
• Synonyms
• Contextual search

Hybrid Search

Combines:

• Keyword search
• Semantic search
• Vector search

Hybrid search often produces the best enterprise retrieval performance.

Common Configuration Mistakes

Candidates should recognize poor implementations.

Examples include:

Using outdated documentation

Result:

Incorrect AI answers.

Uploading duplicate manuals

Result:

Conflicting responses.

Ignoring permissions

Result:

Unauthorized information exposure.

Poor document organization

Result:

Low-quality retrieval.

Overly large documents

Result:

Reduced relevance.

Troubleshooting Knowledge Sources

Problem

Agent cannot answer a question.

Possible causes:

• Document not indexed
• Missing permissions
• Wrong connector
• Unsupported format
• Source disconnected

Problem

Hallucinated response

Possible causes:

• Missing knowledge
• Poor prompt
• Weak grounding
• No matching documents

Problem

Wrong document selected

Possible causes:

• Duplicate information
• Ambiguous wording
• Similar document titles
• Poor organization

Problem

Outdated answer

Possible causes:

• Old document version
• Knowledge source not refreshed
• Multiple conflicting documents

Performance Best Practices

Improve response quality by:

• Removing duplicate documents
• Updating stale content
• Using descriptive document names
• Applying metadata where supported
• Organizing repositories logically
• Limiting unnecessary repositories
• Maintaining clean document libraries

Designing for Scalability

As organizations grow, knowledge repositories also expand.

Scalable designs include:

• Department-specific repositories
• Central governance
• Standard document templates
• Regular review cycles
• Automated indexing
• Consistent naming conventions

Large enterprises often combine SharePoint, Azure AI Search, Dataverse, and external systems into a unified knowledge architecture.

Relationship to Other AB-620 Objectives

This topic connects directly with several other exam areas.

Exam Tips

Remember these important distinctions:

A common exam question presents several repositories and asks which one is the most appropriate. Focus on understanding the business scenario rather than memorizing product names.

Final Review

Before taking the AB-620 exam, ensure you can:

• Explain Retrieval-Augmented Generation (RAG).
• Differentiate structured and unstructured knowledge.
• Compare SharePoint, Dataverse, Azure AI Search, websites, and uploaded files.
• Recommend the correct knowledge source for various business scenarios.
• Explain how prompts and knowledge sources complement each other.
• Describe governance and security considerations.
• Identify causes of hallucinations and inaccurate responses.
• Apply best practices for organizing enterprise knowledge.

Practice Exam Questions

Question 1

A company stores over five million engineering documents across multiple repositories. Users need semantic search with highly relevant AI-generated answers.

Which knowledge solution is the best choice?

A. Uploaded PDF files

B. SharePoint document library only

C. Azure AI Search

D. Dataverse tables

Correct Answer: C

Explanation:
Azure AI Search is designed for enterprise-scale indexing, semantic search, vector search, and retrieval across massive document collections. Uploaded files and SharePoint alone are less suitable for large-scale enterprise search.

Question 2

What is the primary purpose of a custom knowledge source in Copilot Studio?

A. Execute Power Automate flows

B. Provide factual information that grounds AI-generated responses

C. Improve connector authentication

D. Replace topic triggers

Correct Answer: B

Explanation:
Knowledge sources provide trusted information used during Retrieval-Augmented Generation (RAG). They do not execute workflows or replace conversational triggers.

Question 3

A developer wants an agent to answer questions using the latest employee handbook stored in Microsoft 365.

Which repository is the most appropriate?

A. Azure AI Search

B. Uploaded Excel workbook

C. SharePoint

D. Dataverse

Correct Answer: C

Explanation:
SharePoint is the preferred repository for organizational documents that change regularly and already inherit Microsoft 365 security.

Question 4

Which statement best describes the relationship between custom prompts and custom knowledge?

A. They perform identical functions.

B. Custom prompts retrieve documents.

C. Custom knowledge replaces large language models.

D. Custom prompts influence how responses are generated, while custom knowledge provides the factual information used to generate them.

Correct Answer: D

Explanation:
Prompts guide the model’s behavior and formatting, while knowledge sources provide the content used to create accurate, grounded responses.

Question 5

Which practice most improves AI retrieval quality?

A. Store every policy in one large document.

B. Duplicate documents across multiple repositories.

C. Divide documentation into well-organized, topic-specific documents.

D. Remove document headings.

Correct Answer: C

Explanation:
Smaller, clearly organized documents improve retrieval precision and reduce ambiguity during grounding.

Question 6

A user receives information they should not have been able to access.

Which security principle was most likely violated?

A. Document versioning

B. Semantic indexing

C. Retrieval-Augmented Generation

D. Least privilege

Correct Answer: D

Explanation:
Least privilege ensures users can access only the information necessary for their role. Violating this principle can expose sensitive information.

Question 7

An AI agent consistently provides outdated answers despite having the correct repository configured.

What is the most likely cause?

A. The documents have not been updated or re-indexed.

B. The custom prompt is too short.

C. The topic trigger contains multiple phrases.

D. The conversation variables are empty.

Correct Answer: A

Explanation:
If the repository contains outdated content or has not been refreshed, the AI will continue retrieving stale information.

Question 8

Which Azure AI Search capability helps locate conceptually similar information even when exact keywords are absent?

A. Power Automate

B. Keyword ranking

C. Vector search

D. Adaptive Cards

Correct Answer: C

Explanation:
Vector search uses embeddings to identify semantically related content rather than relying solely on exact keyword matches.

Question 9

Which repository is best suited for storing structured business records such as products, customers, and inventory?

A. SharePoint

B. Public websites

C. Uploaded PDF documents

D. Dataverse

Correct Answer: D

Explanation:
Dataverse is designed to manage structured relational business data and is ideal for operational records.

Question 10

A company wants to reduce hallucinations in AI-generated responses.

Which approach best supports this objective?

A. Use larger custom prompts only.

B. Disable knowledge sources.

C. Ground responses using trusted enterprise knowledge repositories.

D. Increase the number of topic triggers.

Correct Answer: C

Explanation:
Grounding responses with trusted enterprise knowledge is one of the most effective ways to reduce hallucinations and improve the reliability and accuracy of AI-generated answers.

Key Takeaways

For the AB-620 exam, remember these core principles:

• Grounding with custom knowledge sources improves accuracy, consistency, and trustworthiness.
• Choose knowledge repositories based on the type, scale, and location of the information.
• Custom prompts define how an agent responds; custom knowledge defines what it responds with.
• Azure AI Search is the preferred solution for large-scale, enterprise-grade semantic and vector search.
• Organize knowledge into clear, well-maintained documents to maximize retrieval quality.
• Respect authentication, authorization, and governance requirements to ensure secure access to enterprise knowledge.
• Retrieval-Augmented Generation (RAG) is a foundational concept for designing intelligent, enterprise-ready agents in Microsoft Copilot Studio.

Go to the AB-620 Exam Prep Hub main page