Microsoft AI Transformation Leader (AB-731) Exam Questions

Microsoft AI Transformation Leader Exam Objectives

1. 1

   1.0 Identify the business value of generative AI solutions (35-40%)

   • 1.1Identify the foundational concepts of generative AI
   • 1.2Describe the differences between generative AI and other types of AI
   • 1.3Select a generative AI solution to meet a business need
   • 1.4Describe the differences between AI models, including fine-tuned and pretrained models
   • 1.5Explain the cost drivers in generative AI usage, including tokens and return-on-investment (ROI) considerations
   • 1.6Identify the challenges of using generative AI solutions, including fabrications, reliability, and bias
   • 1.7Identify when generative AI solutions can provide business value, including scalability and automation
   • 1.8Identify benefits and capabilities of generative AI solutions
   • 1.9Describe the impact of prompt engineering
   • 1.10Understand techniques of prompt engineering
   • 1.11Identify business requirements for grounding solutions
   • 1.12Understand how retrieval-augmented generation (RAG) is used for AI solutions
   • 1.13Understand the impact of data on AI solutions, including data type, data quality, and representative datasets
   • 1.14Describe the importance of secure AI
   • 1.15Identify scenarios when machine learning adds value
   • 1.16Describe the lifecycle of a machine learning solution
   • 1.17Identify security considerations for AI systems, including application security, data security, and authentication requirements
2. 2

   2.0 Identify benefits, capabilities, and opportunities for Microsofts AI apps and services (35-40%)

   • 2.1Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot
   • 2.2Map business processes and use cases to Copilot
   • 2.3Understand differences in capabilities between versions of Copilot
   • 2.4Understand capabilities of Microsoft 365 Copilot Chat web and mobile experiences
   • 2.5Understand capabilities of the Copilot experience in various Microsoft 365 apps
   • 2.6Understand capabilities of Microsoft Copilot Studio
   • 2.7Understand capabilities of Microsoft Graph
   • 2.8Identify benefits and capabilities of an integrated Microsoft AI solution, including risk mitigation and safety benefits
   • 2.9Map business processes and use cases to Microsoft’s AI apps and services
   • 2.10Identify when to use Researcher or Analyst in Copilot
   • 2.11Identify when to build, buy, or extend, including the Microsoft 365 Copilot extensibility framework
   • 2.12Identify benefits and capabilities of Foundry Tools
   • 2.13Map business processes and use cases to Foundry Tools
   • 2.14Identify capabilities of Azure AI services, including Azure Vision in Foundry Tools, Azure AI Search, and Microsoft Foundry
   • 2.15Match an AI model to a business need
   • 2.16Identify the benefits of Microsoft Foundry and Foundry Tools, including scalability and security
3. 3

   3.0 Identify an implementation and adoption strategy for Microsofts AI apps and services (20-25%)

   • 3.1Align an AI strategy with Microsoft responsible AI policies
   • 3.2Explain the importance of responsible AI
   • 3.3Establish governance principles for AI use
   • 3.4Establish an AI council to guide strategy, oversight, and cross-functional alignment
   • 3.5Ensure that AI solutions meet responsible AI standards, including fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability
   • 3.6Plan for AI adoption across the organization
   • 3.7Establish an adoption team
   • 3.8Identify common barriers to adoption
   • 3.9Establish an AI champions program
   • 3.10Understand potential impacts to data, security, privacy, and cost
   • 3.11Understand Copilot license types, including pay-as-you go, monthly, and included with Microsoft 365 subscription
   • 3.12Understand Azure AI services subscription models, including pay-as-you-go and prepaid