AB-731 Exam Prep Guide: Microsoft AI Transformation Leader

Compare artificial intelligence, machine learning, and generative AI, clarifying their relationships, data inputs, outputs, and use cases, including accuracy considerations and the impact of large language models.

Leaders learn to choose the right generative AI by balancing pre-trained and fine-tuned models to meet business needs, using a decision framework on problem, accuracy, cost, and privacy.

Evaluate generative ai using token-based pricing to measure input-output usage and determine roi and value created. Scale thoughtfully and track productivity gains and time savings to justify the investment.

Learn how hallucinations and bias in generative AI pose business risks, and how leaders mitigate them with grounded data, governance, and human review.

Learn how prompt engineering crafts precise prompts to guide AI models like chat GPT toward accurate, useful, and creative outputs, with tips on crafting effective questions and choosing words and context.

Master best practices for writing prompts by being clear and specific, providing context, asking direct questions, using simple language, and breaking down complex queries.

Master zero-sort prompting, one-sort prompting, few-sort prompting, and role-based prompting to boost AI task understanding and output quality.

Grounding connects AI responses to trusted external sources, while retrieval-augmented generation implements this by fetching relevant data before answering, improving accuracy and reducing hallucination in enterprise use.

Secure AI protects enterprise data and enables adoption by preventing data leakage, unauthorized access, prompt injection, supply chain risk, and insider misuse through RBAC, encryption, audit logs, and trusted providers.

Master data privacy and security for generative AI with GDPR rules, data planning, and risk assessment. Implement minimal data collection, strong protections, and breach-response plans to protect users and trust.

Explore Microsoft Copilot, an AI-powered conversational assistant—part of a family of Copilot flavors—that enhances Microsoft 365, Azure, GitHub, and Windows with prompt-based text, image, and video generation.

Explore how Microsoft Graph, a unified API, connects all Microsoft 365 services via a single gateway at graph.microsoft.com, enabling access to emails, files, and Teams data with permissions.

Compare Copilot versions from free and pro to Microsoft 365 Copilot and Copilot Studio, detailing enterprise features, pricing, data connections, and use cases like meeting summaries and email drafting.

Explore Copilot Studio, a low-code platform to build custom copilots and AI agents grounded in your data, with connectors, multi-channel publishing, governance, for HR, IT, sales, and customer service.

Microsoft integrates AI into trusted platforms like Microsoft 365 and Teams, keeping existing security policies, data privacy, access management, compliance, and content safety built in.

Decide whether to buy, build, or extend Microsoft 365 Copilot using Copilot Studio, connecting to business data and apps like Salesforce or Jira to fit your needs.

Use Microsoft Copilot to summarize long email threads, saving IT helpdesk time by turning complex conversations into concise five-line summaries, enabling faster ticket handling.

Explore how Excel Copilot adds data with formulas, like combining category and product into a new column, inserting dates and counting days to today, with formula explanations for learning.

Explore how Copilot formats data in Excel, applying conditional formatting like red for ratings under 70% and green for ratings over 90%, and hides rows by sales thresholds.

Copilot transforms teams meetings by automatically summarizing discussions, extracting action items, and generating meeting notes in seconds, boosting productivity and accuracy.

Explore how Copilot in Microsoft Streams and Teams recaps meetings, summarizes transcripts, and lists action items with prompts for understanding and bulleted summaries.

Microsoft Foundry is a unified enterprise AI platform that brings together agent building, model access, grounding, and governance in one workspace.

Discover how Microsoft Foundry scales AI deployments from a 10-user pilot to 10,000 users across 12 countries, with built-in security and GDPR, ISO 27001, SOC 2, HIPAA compliance.

Map business processes to Foundry tools, identifying the best tool for each use case. Learn through examples like customer support chatbots with Foundry and grounding with Foundry IQ.

Explore why responsible AI matters in enterprise by applying fair, safe, transparent, and accountable principles to reduce risk, protect data, and build trust for successful AI adoption.

Empower AI that prioritizes human well-being through transparency and accountability, teaching how to apply human-centric values in generative AI and responsible governance.

Define bias in AI and show how generative AI can reflect gender, race, or other social factors, and identify training data, algorithm bias, and predictive bias.

Learn how an AI adoption team drives successful AI transformation by training, measuring adoption, and gathering feedback to turn strategy into real business value.

Explore how ai transformation impacts data, security, privacy, and cost, and plan a unified data foundation with Microsoft Fabric and OneLake for successful ai adoption.