AB-100 Preparation Details
Preparing for the AB-100 Agentic AI Business Solutions Architect certification exam? Start here with a complete, objective-wise AB-100 study guide designed to help you pass faster.
This guide brings together official Microsoft documentation, key concepts, and curated resources for every AB-100 exam objective, making it ideal for both beginners and last-minute revision.
Looking for the best AB-100 preparation resources in one place? This page covers everything you need to get exam-ready with confidence.
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AB-100 Agentic AI Materials
Plan AI-powered business solutions (25–30%)
Analyze requirements for AI-powered business solutions
Assess the use of agents in task automation, data analytics, and decision-making
AI Agent Adoption Guidance for Organizations
Review data for grounding, including accuracy, relevance, timeliness, cleanliness, and availability
Grounding data design for AI workloads on Azure
Data architecture for AI agents across your organization
RAG and Generative AI – Azure AI Search
Organize business solution data to be available for other AI systems
Data architecture for AI agents across your organization
Technology plan for AI agents across your organization
Design overall AI strategy for business solutions
Implement the AI adoption process from the Cloud Adoption Framework for Azure
AI adoption for Microsoft and Azure – Cloud Adoption Framework
Plan for AI adoption – Cloud Adoption Framework
Design the strategy for building AI and agents in business solutions
Create your AI strategy – Cloud Adoption Framework
Business plan for AI agents – Cloud Adoption Framework
Design a multi-agent solution by using platforms such as Microsoft 365 Copilot, Copilot Studio, and Microsoft Foundry
Build a Multiple-Agent Workflow Automation Solution using Microsoft Agent Framework
Technology plan for AI agents across your organization
Develop the use cases for prebuilt agents in the solution
Agents for Microsoft 365 Copilot
Agents, Copilot, and AI capabilities in Dynamics 365 apps
Define the solution rules and constraints when building AI components with Copilot Studio, Microsoft Foundry and Foundry Tools
What is Azure AI Foundry Agent Service?
Overview – Microsoft Copilot Studio
Determine the use of generative AI and knowledge sources in agents built with Copilot Studio
Prompts overview – Microsoft Copilot Studio
Add knowledge sources to your declarative agent in Microsoft 365 Copilot
Determine when to build custom agents or extend Microsoft 365 Copilot
Agents for Microsoft 365 Copilot
Your extensibility options for Microsoft 365 Copilot
Determine when custom AI models should be created
Getting started with customizing a large language model (LLM)
Concepts – Small and large language models – Azure Kubernetes Service
Provide guidelines for creating a prompt library
Get started with prompt library – Microsoft Copilot Studio
Get started with prompt library – AI Builder
Develop the use cases for customized small language models for the solution
Use local small language models (SLMs) in Azure App Service
Concepts – Small and large language models – Azure Kubernetes Service
Provide prompt engineering guidelines and techniques for AI-powered business solutions
Prompt engineering techniques – Microsoft Foundry
Getting started with LLM prompt engineering
Include the elements of the Microsoft AI Center of Excellence
Establish an AI Center of Excellence – Cloud Adoption Framework
AI Center of Excellence – Training
Introduction to AI Center of Excellence
Design AI solutions that use multiple Dynamics 365 apps
Agents, Copilot, and AI capabilities in Dynamics 365 apps
Architect AI Solutions For Business Productivity
Evaluate the costs and benefits of an AI-powered business solution
Select ROI criteria for AI-powered business solutions, including the total cost of ownership
Maximize the Cost Efficiency of AI Agents on Azure
Manage AI – Cloud Adoption Framework
Create an ROI analysis for the proposed AI solution for a business process
Forecast the Return on Investment (ROI) of AI Agents
Architect AI Solutions For Business Productivity
Analyze whether to build, buy, or extend AI components for business solutions
Technology plan for AI agents across your organization
Maximize the Cost Efficiency of AI Agents on Azure
Implement a model router to intelligently route requests to the most suitable model
Model router for Microsoft Foundry – concepts
How to use model router for Microsoft Foundry
Design AI-powered business solutions (25–30%)
Design AI and agents for business solutions
Design business terms for Copilot in Dynamics 365 apps for customer experience and service
Agents, Copilot, and AI capabilities in Dynamics 365 apps
Copilot in Dynamics 365 Customer Service architecture
Design customizations of Copilot in Dynamics 365 apps for customer experience and service
Manage Copilot features in Customer Service
Welcome to Service in Microsoft 365 Copilot
Design connectors for Copilot in Dynamics 365 Sales
Turn on and set up Copilot in Dynamics 365 Sales
Sales agent deployment guide for Dynamics 365 customers
Design agents for integration with Dynamics 365 Contact Center channels
Integrate a Copilot agent in Dynamics 365 Contact Center
Set up IVR agents in the voice channel using Copilot Studio
Design task agents
Agent flows overview – Microsoft Copilot Studio
Add tools to custom agents – Microsoft Copilot Studio
Design autonomous agents
Design autonomous agent capabilities – Microsoft Copilot Studio
Build an autonomous agent in Copilot Studio
Design prompt and response agents
Prompts overview – Microsoft Copilot Studio
Use prompts to make your agent perform specific tasks
Propose Foundry Tools for a given requirement
What is Azure AI Foundry Agent Service?
Propose code-first generative pages and the use of an agent feed for apps
Overview of Power Platform 2025 release wave 2
Use Copilot Studio agents in model-driven apps
Design topics for Copilot Studio, including fallback
Configure the system fallback topic
Design data processing for AI models and grounding
Grounding data design for AI workloads on Azure
RAG and generative AI – Azure AI Search
Design a business process to include AI components in a Power Apps canvas app
Overview of AI Builder in Power Apps
Use Copilot Studio agents in model-driven apps
Apply the Microsoft Power Platform Well-Architected Framework to intelligent application workloads
Overview of intelligent application workloads – Power Platform
Design principles for intelligent application workloads
Determine when to use standard NLP, Azure conversational language understanding, or generative AI orchestration in Copilot Studio
Orchestrate agent behavior with generative AI
Apply generative orchestration capabilities
Design agents and agent flows with Copilot Studio
Overview – Microsoft Copilot Studio
Agent flows overview – Microsoft Copilot Studio
Design prompt actions in Copilot Studio
Get started with prompt library – Microsoft Copilot Studio
Create a prompt – Microsoft Copilot Studio
Design extensibility of AI solutions
Design AI solutions by using custom models in Microsoft Foundry
Fine-tune models with Microsoft Foundry
Getting started with customizing a large language model (LLM)
Design agents in Microsoft 365 Copilot
Agents for Microsoft 365 Copilot
Your extensibility options for Microsoft 365 Copilot
Design agent extensibility in Copilot Studio
Add other agents overview – Microsoft Copilot Studio
Use agent tools to extend, automate, and enhance your agents
Design agent extensibility with Model Context Protocol in Copilot Studio
Extend your agent with Model Context Protocol
Connect your agent to an existing MCP server
Design agents to automate tasks in apps and websites by using Computer Use in Copilot Studio
Automate web and desktop apps with computer use
Configure where computer use runs
Design agent behaviors in Copilot Studio, including reasoning and voice mode
Real-time voice agents – Microsoft Copilot Studio
Configure voice capabilities – Microsoft Copilot Studio
Apply generative orchestration capabilities
Optimize solution design by using agents in Microsoft 365, including Teams and SharePoint
Optimize solution design with agents in Microsoft 365
Share and manage agents built with Microsoft 365 Copilot
Orchestrate configuration for prebuilt agents and apps
Orchestrate AI features in Dynamics 365 apps for finance and supply chain
Overview of Copilot capabilities in finance and operations apps
Use Model Context Protocol for finance and operations apps
Orchestrate AI features in Dynamics 365 apps for customer experience and service
Agents, Copilot, and AI capabilities in Dynamics 365 apps
Orchestrate Configuration of Prebuilt Agents and Apps
Propose Microsoft 365 agents for business scenarios
Agent Store in Microsoft 365 Copilot
Technology plan for AI agents across your organization
Orchestrate the configuration of Microsoft 365 Copilot for Sales and Microsoft 365 Copilot for Service
Deploy and configure Sales in Microsoft 365 Copilot
Manage Copilot features in Customer Service
Propose Microsoft Power Platform AI features, including AI hub
Copilots and generative AI in Power Platform
Design interoperability of the finance and operations agent chats to use additional knowledge sources
Build an agent with Dynamics 365 ERP MCP
Create AI plugins for copilots with finance and operations business logic
Recommend the process of adding knowledge sources to in-app help and guidance for Dynamics 365 Finance or Dynamics 365 Supply Chain Management apps
Use Model Context Protocol for finance and operations apps
Overview of Copilot capabilities in finance and operations apps
Deploy AI-powered business solutions (40–45%)
Analyze, monitor, and tune AI-powered business solutions
Recommend the process and tools required for monitoring agents
Monitor operations, compliance, and capacity – Microsoft Copilot Studio
Metrics and recommendations for Copilot Studio – Power Platform
Monitor agents using Agent Inventory in Copilot Studio Kit
Analyze backlog and user feedback of AI and agent usage
Analytics overview – Microsoft Copilot Studio
Analyze conversational agent effectiveness – Microsoft Copilot Studio
Apply AI-based tools to analyze and identify issues and perform tuning
Improve your Copilot Studio projects – overview
Analyze your agent with custom metrics
Monitor agent performance and metrics
Analyze autonomous agent performance – Microsoft Copilot Studio
Measure agent outcomes – Microsoft Copilot Studio
Interpret telemetry data for performance and model tuning
Capture telemetry with Application Insights – Microsoft Copilot Studio
Monitor AI Agents with Application Insights – Azure Monitor
Application Insights telemetry with Microsoft Copilot Studio
Manage the testing of AI-powered business solutions
Recommend the process and metrics to test agents
Design a testing strategy for your agents – Microsoft Copilot Studio
About agent evaluation – Microsoft Copilot Studio
Create validation criteria of custom AI models
Review the agent evaluation checklist – Microsoft Copilot Studio
Choose evaluation methods – Microsoft Copilot Studio
Validate effective Copilot prompt best practices
Prompt engineering techniques – Azure OpenAI
Configure high-quality instructions for generative orchestration
Design end-to-end test scenarios of AI solutions that use multiple Dynamics 365 apps
Configure tests in Copilot Studio Kit
Enhance agent testing with Copilot Studio Kit
Build the strategy for creating test cases by using Copilot
Generate and import test sets for agent testing
Run tests and view results – Microsoft Copilot Studio
Design the ALM process for AI-powered business solutions
Design the ALM process for data used in AI models and agents
Data architecture for AI agents across your organization
Design ALM Process for AI-Powered Business Solutions – Training
Design the ALM process for Copilot Studio agents, connectors, and actions
Establish an ALM strategy – Microsoft Copilot Studio
Create and manage custom solutions – Microsoft Copilot Studio
Design the ALM process for Microsoft Foundry agents
Source Control, CI/CD, and ALM for Fabric Data Agent
Process to build agents across your organization with Microsoft Foundry and Copilot Studio
Design the ALM process for custom AI models
Application lifecycle management with Microsoft Power Platform
Deployment and test considerations for intelligent application workloads
Design the ALM process for AI in Dynamics 365 apps for finance and supply chain
Use Model Context Protocol for finance and operations apps
Build an agent with Dynamics 365 ERP MCP
Design the ALM process for AI in Dynamics 365 apps for customer experience and service
Integrate a Copilot agent in Dynamics 365 Contact Center
Manage Copilot features in Customer Service
Design responsible AI, security, governance, risk management, and compliance
Design security for agents
Secure AI agents at scale using Microsoft Agent 365
Microsoft Entra security for AI overview
Design governance for agents
Governance and security for AI agents across the organization
Security and governance – Microsoft Copilot Studio
Design model security
Azure AI security best practices
AI shared responsibility model
Analyze solution and AI vulnerabilities and mitigations, including prompt manipulation
Reduce autonomous agentic AI risk
Secure autonomous agentic AI systems
Review solution for adherence to responsible AI principles
Responsible AI in Azure Workloads – Azure Well-Architected Framework
Identify guiding principles for responsible AI
Validate data residency and movement compliance
Data, Privacy, and Security for Microsoft 365 Copilot
Governance and security for AI agents across the organization
Design access controls on grounding data and model tuning
Security for AI agents with Microsoft Entra Agent ID
Grounding data design for AI workloads on Azure
Design audit trails for changes to models and data
Azure AI security best practices
Responsible AI in Azure Workloads – Azure Well-Architected Framework
This brings us to the end of the AB-100 Agentic AI Business Solutions Architect Study Guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!
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