AB-731 Preparation Details
Preparing for the AB-731 AI Transformation Leader certification exam? Start here with a complete, objective-wise AB-731 study guide designed to help you pass faster.
This guide brings together official Microsoft documentation, key concepts, and curated resources for every AB-731 exam objective, making it ideal for both beginners and last-minute revision.
Looking for the best AB-731 preparation resources in one place? This page covers everything you need to get exam-ready with confidence.
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AB-731 AI Leader Materials
Identify the business value of generative AI solutions (35–40%)
Identify the foundational concepts of generative AI
Describe the differences between generative AI and other types of AI
AI fluency: Explore generative AI
Deep Learning vs. Machine Learning
Select a generative AI solution to meet a business need
Understand the Foundations of Generative AI for Business Leaders
Generative AI Applications for Developers
Describe the differences between AI models, including fine-tuned and pretrained models
Fine-tune models with Microsoft Foundry
Introduction to generative AI and agents
Explain the cost drivers in generative AI usage, including tokens and ROI considerations
Key concepts and considerations in generative AI
Maximize the Cost Efficiency of AI Agents on Azure
Identify the challenges of using generative AI solutions, including fabrications, reliability, and bias
Understand the Foundations of Generative AI for Business Leaders
What is Responsible AI – Azure Machine Learning
Identify when generative AI solutions can provide business value, including scalability and automation
Explore the Business Value of Generative AI Solutions
Identify benefits and capabilities of generative AI solutions
Describe the impact of prompt engineering
Getting started with LLM prompt engineering
Prompt engineering techniques – Azure OpenAI
Understand techniques of prompt engineering
Prompt engineering techniques – Microsoft Foundry
System message design for Azure OpenAI
Identify business requirements for grounding solutions
RAG and generative AI – Azure AI Search
Retrieval augmented generation (RAG) and indexes in Microsoft Foundry
Understand how retrieval-augmented generation (RAG) is used for AI solutions
Develop a RAG-based solution with your own data using Microsoft Foundry
Design and Develop a RAG Solution
Understand the impact of data on AI solutions, including data type, data quality, and representative datasets
What is Responsible AI – Azure Machine Learning
Describe the importance of secure AI
Azure AI security best practices
AI shared responsibility model
Identify scenarios when machine learning adds value
Explore the Business Value of Generative AI Solutions
Describe the lifecycle of a machine learning solution
What is Responsible AI – Azure Machine Learning
Identify security considerations for AI systems, including application security, data security, and authentication requirements
Azure AI security best practices
Threat Modeling AI/ML Systems and Dependencies
Identify benefits, capabilities, and opportunities for Microsoft’s AI apps and services (35–40%)
Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot
Map business processes and use cases to Copilot
Empower your workforce with Microsoft 365 Copilot Use Cases
Define the role you want Microsoft 365 Copilot to play in your business workflow
Understand differences in capabilities between versions of Copilot
Decide which Copilot is right for you
Overview of Microsoft 365 Copilot Chat
Understand capabilities of Microsoft 365 Copilot Chat web and mobile experiences
Overview of Microsoft 365 Copilot Chat
Frequently asked questions about Microsoft 365 Copilot Chat
Understand capabilities of the Copilot experience in various Microsoft 365 apps
What is Microsoft 365 Copilot?
Microsoft 365 Copilot – Service Descriptions
Understand capabilities of Microsoft Copilot Studio
Overview – Microsoft Copilot Studio
Official Microsoft Copilot Studio documentation
Understand capabilities of Microsoft Graph
Major services and features in Microsoft Graph
Identify benefits and capabilities of an integrated Microsoft AI solution, including risk mitigation and safety benefits
Application card: Microsoft 365 Copilot
Security for Microsoft 365 Copilot
Map business processes and use cases to Microsoft’s AI apps and services
Empower your workforce with Microsoft 365 Copilot Use Cases
Explore the Business Value of Generative AI Solutions
Identify when to use Researcher or Analyst in Copilot
What is Researcher Agent in Microsoft 365 Copilot?
Microsoft 365 Copilot Researcher agent frequently asked questions
Identify when to build, buy, or extend, including the Microsoft 365 Copilot extensibility framework
Microsoft 365 Copilot Extensibility Planning Guide
Your extensibility options for Microsoft 365 Copilot
Identify benefits and capabilities of Foundry Tools
Map business processes and use cases to Foundry Tools
Explore the Business Value of Generative AI Solutions
Identify capabilities of Azure AI services, including Azure Vision in Foundry Tools, Azure AI Search, and Microsoft Foundry
What is Azure Vision in Foundry Tools?
Introduction to Azure AI Search
Match an AI model to a business need
Microsoft Foundry Models overview
Fine-tune models with Microsoft Foundry
Identify the benefits of Microsoft Foundry and Foundry Tools, including scalability and security
Microsoft Foundry architecture
Identify an implementation and adoption strategy for Microsoft’s AI apps and services (20–25%)
Align an AI strategy with Microsoft responsible AI policies
Explain the importance of responsible AI
Embrace Responsible AI Principles and Practices
Artificial Intelligence overview – Microsoft Service Assurance
Establish governance principles for AI use
Govern AI – Cloud Adoption Framework
Adopt responsible and trusted AI principles
Establish an AI council to guide strategy, oversight, and cross-functional alignment
Establishing Responsible AI Policies for AI Agents across Organizations
Agentic AI maturity model – AI governance and security
Ensure that AI solutions meet responsible AI standards, including fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability
Identify guiding principles for responsible AI
What is Responsible AI – Azure Machine Learning
Plan for AI adoption across the organization
Establish an adoption team
Microsoft 365 Copilot adoption and onboarding guide for IT admins
Plan for AI adoption – Cloud Adoption Framework
Identify common barriers to adoption
Explore user enablement strategies for adopting Microsoft 365 Copilot
Maturity Model for Microsoft 365 – Implementing Microsoft 365 Copilot Organization-Wide
Establish an AI champions program
Agentic AI maturity model – Organizational readiness and culture
Establishing Responsible AI Policies for AI Agents across Organizations
Understand potential impacts to data, security, privacy, and cost
Governance and security for AI agents across the organization
Plan and Manage Costs – Microsoft Foundry
Understand Copilot license types, including pay-as-you-go, monthly, and included with Microsoft 365 subscription
License Options for Microsoft 365 Copilot
Microsoft 365 Copilot pay-as-you-go overview
Understand Azure AI services subscription models, including pay-as-you-go and prepaid
Use Foundry Tools with commitment tier pricing
Plan and manage costs for Azure AI Foundry
This brings us to the end of the AB-731 AI Transformational Leader 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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