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Microsoft Azure AI Solutions Architect AZ-305

Microsoft via Coursera Professional Certificate

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Go beyond learning Azure AI services to build the architectural decision making skills needed to design, evaluate, secure, and govern enterprise AI solutions. Using Microsoft Foundry, Azure OpenAI Service, and the Azure AI portfolio, you’ll work with RAG pipelines, agentic workflows, NLP, speech, vision, and knowledge mining. Designed for cloud architects, AI engineers, software developers, and technical consultants, this program prepares learners to target Azure AI Solutions Architect and AI Solutions Architect roles while supporting select skills covered in Microsoft Exam AZ 305. Through real world scenarios in financial services, healthcare, legal, and enterprise IT, you’ll apply structured decision frameworks to select services, model costs, configure security, enforce governance, and operationalize AI at scale. Four architecture projects help you build portfolio ready diagrams, design specifications, and solution documents. By the end, you’ll be equipped to lead AI architecture conversations, evaluate design tradeoffs, and deliver enterprise solutions that meet performance, compliance, and safety requirements.

Syllabus

Course 1: Azure AI Foundations & Solution Planning
Course 2: Core AI Solution Design
Course 3: Cognitive & Knowledge Services
Course 4: Agentic AI, Security, Governance & Operations
Course 5: Launch Your Azure AI Solutions Architect Career

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Establish the architectural foundation for enterprise Azure AI solution design. This course maps the full Azure AI service portfolio, including Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and the specialized Azure AI services, and develops the solution planning and cost modeling skills needed to move from business requirements to a defensible architecture recommendation. You'll design a Microsoft Foundry workspace architecture with identity configuration and governance controls, analyze enterprise requirements against compliance constraints including HIPAA and data residency, and apply build-vs-buy-vs-configure decision frameworks and token economics to solution planning. No prior AI architect experience is required. However, foundational Azure familiarity is expected.
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Design the core architectural components of production Azure AI solutions. This course explores four disciplines central to enterprise AI architecture: model evaluation and deployment considerations, retrieval augmented generation (RAG), prompt design and evaluation, and model customization strategies. Through enterprise scenarios, you’ll apply structured decision frameworks to evaluate Azure OpenAI deployment options for multi use case environments, assess RAG architectures using Azure AI Search and Foundry IQ, develop effective prompt strategies using evaluation frameworks for quality, safety, and groundedness, and compare fine tuning and RAG approaches to inform architectural decisions. A GenAI Literacy module is also included to help you apply generative AI tools to your own architecture work.