
Generative AI creates new content and differs from traditional AI, which analyzes data for fraud detection, spam filtering, and image recognition, using prompts to generate code, text, and images.
Learn how large language models predict the next token from context and why they do not truly know content, and how neural networks and transformers process prompts into code.
Identify core AI risks such as over-reliance, hallucinations, bias, misinformation, and disinformation, and examine cybersecurity threats like prompt injection and data disclosure in enterprise contexts.
Explore how machine learning merges data science and software engineering to build predictive models, with uses like forecasting and anomaly detection, and Gen AI as a subset of ML.
Map business processes to Copilot by exploring how M365 Copilot, integrated with Microsoft Graph, grounds prompts, ensures compliance, and enhances productivity across Outlook, Word, Excel, Teams, and more.
Explore how Microsoft Copilot Studio enables non-coders to build AI agents and agent flows with a low-code interface, connecting APIs, webhooks, and end-to-end workflows.
Explore how the Microsoft Graph API grounds Microsoft 365 Copilot by using user identity, devices, installed apps, and organization data to tailor responses and ensure compliance.
Explore Azure regions, data centers, and the Azure backbone, highlighting availability zones, pad regions, geography, and key compliance like GDPR, HIPAA, and PCI DSS.
Explore the Azure resource hierarchy, from root management group to subscriptions, resource groups, and resources, and apply governance with policy and RBAC for secure, cost effective workloads.
Learn how to create an Azure subscription by starting free or pay-as-you-go, with 12 months of popular services, always free options, and a $200 credit for 30 days.
Create and scope an Azure cost management budget for a subscription, set monthly (or quarterly) periods, thresholds, and alerts for actual or forecasted spend—without stopping resources.
Learn how Foundry IQ adds a unified knowledge layer for agents with multi-hop retrieval, source routing, and permission-aware access across data sources like One Lake and SharePoint.
Identify sensitive ai workloads with a sensitivity flag, assess impact and risk, and apply mitigation under the ai ethics office to govern ai responsibly at scale.
This course contains the use of artificial intelligence.
This AB-731 course by Christopher Nett is a meticulously organized Udemy course designed for IT professionals aiming to pass the Microsoft AB-731 exam. This course systematically guides you from the basics to advanced concepts of AI.
By mastering Microsoft AI services, you're developing expertise in essential topics in today's IT and business landscape.
The course is always aligned with Microsoft's latest study guide and exam objectives:
Identify the foundational concepts of generative AI
Describe the differences between generative AI and other types of AI
Select a generative AI solution to meet a business need
Describe the differences between AI models, including fine-tuned and pretrained models
Explain the cost drivers in generative AI usage, including tokens and return-on-investment (ROI) considerations
Identify the challenges of using generative AI solutions, including fabrications, reliability, and bias
Identify when generative AI solutions can provide business value, including scalability and automation
Identify benefits and capabilities of generative AI solutions
Describe the impact of prompt engineering
Understand techniques of prompt engineering
Identify business requirements for grounding solutions
Understand how retrieval-augmented generation (RAG) is used for AI solutions
Understand the impact of data on AI solutions, including data type, data quality, and representative datasets
Describe the importance of secure AI
Identify scenarios when machine learning adds value
Describe the lifecycle of a machine learning solution
Identify security considerations for AI systems, including application security, data security, and authentication requirements
Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot
Map business processes and use cases to Copilot
Understand differences in capabilities between versions of Copilot
Understand capabilities of Microsoft 365 Copilot Chat web and mobile experiences
Understand capabilities of the Copilot experience in various Microsoft 365 apps
Understand capabilities of Microsoft Copilot Studio
Understand capabilities of Microsoft Graph
Identify benefits and capabilities of an integrated Microsoft AI solution, including risk mitigation and safety benefits
Map business processes and use cases to Microsoft’s AI apps and services
Identify when to use Researcher or Analyst in Copilot
Identify when to build, buy, or extend, including the Microsoft 365 Copilot extensibility framework
Identify benefits and capabilities of Azure AI services
Map business processes and use cases to Azure AI services
Identify capabilities of Azure AI services, including Azure AI Vision, Azure AI Search, and Azure AI Foundry
Match an AI model to a business need
Identify the benefits of Azure AI services for generative AI, including scalability and security
Align an AI strategy with Microsoft responsible AI policies
Explain the importance of responsible AI
Establish governance principles for AI use
Establish an AI council to guide strategy, oversight, and cross-functional alignment
Ensure that AI solutions meet responsible AI standards, including fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability
Plan for AI adoption across the organization
Establish an adoption team
Identify common barriers to adoption
Establish an AI champions program
Understand potential impacts to data, security, privacy, and cost
Understand Copilot license types, including pay-as-you go, monthly, and included with Microsoft 365 subscription
Understand Azure AI services subscription models, including pay-as-you-go and prepaid
This course contains promotional materials.