Microsoft Certified Agentic AI Business Solutions Architect (AB-100) Practice Exam
Microsoft Certified Agentic AI Business Solutions Architect (AB-100) Practice Exam
About the Microsoft Certified Agentic AI Business Solutions Architect (AB-100) Exam
The Microsoft Certified Agentic AI Business Solutions Architect (AB-100) certification validates your expertise in designing, integrating, and implementing AI-driven business solutions that accelerate digital transformation. As a candidate for this certification, you are an accomplished solution architect skilled in leveraging Microsoft AI technologies to design scalable, secure, and enterprise-ready solutions that transform operations, drive innovation, and align with organizational goals.
Who should take the Exam?
This Microsoft (AB-100) certification is ideal for experienced professionals who:
- Architect and deliver AI-first and agentic business solutions.
- Lead enterprise adoption of Microsoft AI, Power Platform, and Dynamics 365.
- Integrate AI agents and multi-agent systems to enhance business processes.
- Ensure compliance with responsible AI frameworks and governance standards.
What are the Core Competencies?
The Microsoft AB-100 core competencies include -
1. AI Solution Architecture
- Design AI-powered architectures using Microsoft Azure AI Services and Azure OpenAI.
- Implement generative AI capabilities aligned with business objectives.
- Develop scalable, secure, and cross-platform AI solutions.
2. Agentic and Multi-Agent Design
- Create agentic-first architectures that leverage autonomous and orchestrated AI agents.
- Use Copilot Studio, AI prompts, and Azure AI Foundry to design intelligent multi-agent solutions.
- Apply Agent2Agent (A2A) and Model Context Protocol (MCP) for interoperability and open communication standards.
3. Integration Across Microsoft Ecosystem
- Combine Dynamics 365, Microsoft Power Platform, and Microsoft Copilot Studio to deliver unified AI business experiences.
- Design end-to-end AI-centric solutions that enhance enterprise productivity and collaboration.
4. Responsible and Secure AI Practices
- Implement Microsoft’s Responsible AI guidelines to ensure fairness, transparency, and accountability.
- Secure AI models and workflows by enforcing data governance, model protection, and audit controls.
- Monitor and mitigate vulnerabilities, including prompt manipulation and data exposure risks.
5. Performance, Optimization, and ROI
- Analyze and interpret telemetry data to monitor agent performance.
- Optimize model reliability and efficiency using continuous improvement frameworks.
- Conduct ROI assessments to measure business impact and solution effectiveness.
Key Responsibilities of a Certified Architect
- Define the AI integration roadmap for enterprise applications.
- Lead the adoption of agentic-first business processes across departments.
- Champion innovation by promoting AI-enabled culture and processes.
- Translate business and technical requirements into comprehensive AI solution architectures.
- Prototype and demonstrate AI-driven components that highlight transformative potential.
- Oversee secure and scalable implementations ensuring alignment with business outcomes.
- Develop a cohesive Application Lifecycle Management (ALM) strategy for AI solutions.
- Define an environment strategy that includes Microsoft and third-party AI tools.
- Guide organizations in establishing governance frameworks for sustainable AI adoption.
Skills Measured
Candidates are evaluated across a broad spectrum of technical and strategic areas, including:
- Architecting agentic-first AI solutions using Microsoft platforms.
- Designing multi-agent orchestration frameworks.
- Implementing responsible AI and compliance controls.
- Integrating AI services across business applications.
- Conducting performance monitoring and ROI analysis.
- Securing AI models, data pipelines, and operational workflows.
Skills Acquired
Earning the Microsoft Certified Agentic AI Business Solutions Architect (AB-100) credential demonstrates mastery in AI-powered solution architecture. It showcases your ability to:
- Lead enterprise AI transformation initiatives.
- Bridge the gap between AI innovation and business strategy.
- Design intelligent, resilient, and value-driven systems that future-proof organizations.
Course Outline
The Microsoft Certified Agentic AI Business Solutions Architect (AB-100) Exam covers the following topics -
Domain 1 - Understanding Plan AI-powered business solutions (25–30%)
1.1 Describe and analyze requirements for AI-powered business solutions
- Explain how to assess the use of agents in task automation, data analytics, and decision-making
- Explain how to review data for grounding, including accuracy, relevance, timeliness, cleanliness, and availability
- Explain how to organize business solution data to be available for other AI systems
1.2 Describe the design of the overall AI strategy for business solutions
- Explain how to implement the AI adoption process from the Cloud Adoption Framework for Azure
- Explain how to design the strategy for building AI and agents in business solutions
- Explain how to design a multi-agent solution by using platforms such as Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry
- Explain how to develop the use cases for prebuilt agents in the solution
- Explain how to define the solution rules and constraints when building AI components with Copilot Studio, Azure AI services, and Azure AI Foundry
- Explain how to determine the use of generative AI and knowledge sources in agents built with Copilot Studio
- Explain how to determine when to build custom agents or extend Microsoft 365 Copilot
- Explain how to determine when custom AI models should be created
- Explain how to provide guidelines for creating a prompt library
- Explain how to develop the use cases for customized small language models for the solution
- Explain how to provide prompt engineering guidelines and techniques for AI-powered business solutions
- Explain how to include the elements of the Microsoft AI Center of Excellence
- Explain how to design AI solutions that use multiple Dynamics 365 apps
1.3 Describe how to evaluate the costs and benefits of an AI-powered business solution
- Explain how to select ROI criteria for AI-powered business solutions, including the total cost of ownership
- Explain how to create an ROI analysis for the proposed AI solution for a business process
- Explain how to analyze whether to build, buy, or extend AI components for business solutions
- Explain how to implement a model router to intelligently route requests to the most suitable model
Domain 2 - Understanding Design AI-powered business solutions (25–30%)
2.1 Describe designing AI and agents for business solutions
- Explain how to design business terms for Copilot in Dynamics 365 apps for customer experience and service
- Explain how to design customizations of Copilot in Dynamics 365 apps for customer experience and service
- Explain how to design connectors for Copilot in Dynamics 365 Sales
- Explain how to design agents for integration with Dynamics 365 Contact Center channels
- Explain how to design task agents
- Explain how to design autonomous agents
- Explain how to design prompt and response agents
- Explain how to propose Microsoft AI services for a given requirement
- Explain how to propose code-first generative pages and the use of an agent feed for apps
- Explain how to design topics for Copilot Studio, including fallback
- Explain how to design data processing for AI models and grounding
- Explain how to design a business process to include AI components in a Power Apps canvas app
- Explain how to apply the Microsoft Power Platform Well-Architected Framework to intelligent application workloads
- Explain how to determine when to use standard natural language processing, Azure conversational language understanding, or generative AI orchestration in Copilot Studio
- Explain how to design agents and agent flows with Copilot Studio
- Explain how to design prompt actions in Copilot Studio
2.2 Describe designing the extensibility of AI solutions
- Explain how to design AI solutions by using custom models in Azure AI Foundry
- Explain how to design agents in Microsoft 365 Copilot
- Explain how to design agent extensibility in Copilot Studio
- Explain how to design agent extensibility with the Model Context Protocol in Copilot Studio
- Explain how to design agents to automate tasks in apps and websites by using Computer Use in Copilot Studio
- Explain how to design agent behaviors in Copilot Studio, including reasoning and voice mode
- Explain how to optimize solution design by using agents in Microsoft 365, including Teams and SharePoint
2.3 Describe the orchestrating configuration for prebuilt agents and apps
- Explain how to orchestrate AI in Dynamics 365 apps for finance and supply chain
- Explain how to orchestrate AI in Dynamics 365 apps for customer experience and service
- Explain how to propose Microsoft 365 agents for business scenarios
- Explain how to orchestrate the configuration of Microsoft 365 Copilot for Sales and Microsoft 365 Copilot for Service
- Explain how to propose Microsoft Power Platform AI features, including AI hub
- Explain how to design interoperability of the finance and operations agent chats to use additional knowledge sources
- Explain how to recommend the process of adding knowledge sources to in-app help and guidance for Dynamics 365 Finance or Dynamics 365 Supply Chain Management apps
Domain 3 - Understanding Deploy AI-powered business solutions (40–45%)
3.1 Describe how to analyze, monitor, and tune AI-powered business solutions
- Explain how to recommend the process and tools required for monitoring agents
- Explain how to analyze backlog and user feedback of AI and agent usage
- Explain how to apply AI-based tools to analyze and identify issues and perform tuning
- Explain how to monitor agent performance and metrics
- Explain how to interpret telemetry data for performance and model tuning
3.2 Describe how to manage the testing of AI-powered business solutions
- Explain how to recommend the process and metrics to test agents
- Explain how to create validation criteria of custom AI models
- Explain how to validate effective Copilot prompt best practices
- Explain how to design end-to-end test scenarios of AI solutions that use multiple Dynamics 365 apps
- Explain how to build the strategy for creating test cases by using Copilot
3.3 Describe how to design the ALM process for AI-powered business solutions
- Explain how to design the ALM process for data used in AI models and agents
- Explain how to design the ALM process for Copilot Studio agents, connectors, and actions
- Explain how to design the ALM process for Azure AI services agents
- Explain how to design the ALM process for custom AI models
- Explain how to design the ALM process for AI in Dynamics 365 apps for finance and supply chain
- Explain how to design the ALM process for AI in Dynamics 365 apps for customer experience and service
3.4 Describe to design of responsible AI, security, governance, risk management, and compliance
- Explain how to design security for agents
- Explain how to design governance for agents
- Explain how to design a model security system
- Explain how to analyze solution and AI vulnerabilities and mitigations, including prompt manipulation
- Explain how to review the solution for adherence to responsible AI principles
- Explain how to validate data residency and movement compliance
- Explain how to design access controls on grounding data and model tuning
- Explain how to design audit trails for changes to models and data
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