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Ai-103: Azure Ai App & Agent Developer Associate Masterclass
Published 7/2026
Created by Sachin Kumar
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 88 Lectures ( 10h 26m ) | Size: 7.2 GB
Master the AI-103 Exam: Build Production-Ready AI Agents, Semantic Kernel Workflows & Enterprise RAG on Azure AI Foundry
What you'll learn
⚡ Pass the AI-103 Exam: Master all three core domains of the Microsoft Azure AI App and Agent Developer Associate certification with expert-led exam prep
⚡ Master Azure AI Foundry: Navigate the unified AI workspace to manage the full project lifecycle, from model selection (GPT-5, Phi-4) to production deployment.
⚡ Build Autonomous Agents: Design sophisticated AI agents that move beyond simple chat to autonomous task planning, tool calling, and internal API integration.
⚡ Implement Advanced RAG: Solve knowledge cutoff issues by building Retrieval-Augmented Generation solutions using Azure AI Search and high-accuracy vector indexi
⚡ Orchestrate Multi-Agent Systems: Deploy complex Manager-Worker patterns and handle conflict resolution between multiple specialized AI agents in a single ecosys
⚡ Develop with Semantic Kernel: Utilize plugins, planners, and memory management to create robust, multi-step reasoning workflows and custom enterprise copilots.
⚡ Apply Responsible AI Standards: Configure content safety filters, jailbreak detection, and fairness testing to ensure your AI applications are secure and ethica
⚡ Architect Generative AI Apps: Learn the "Agent-First" economy shift, mastering prompt engineering, token management, and cost-efficient cloud scaling strategies
Requirements
❗ Foundational Cloud Knowledge: A basic understanding of Microsoft Azure services (equivalent to AZ-900 or AI-900) is recommended.
❗ Programming Basics: Familiarity with Python is essential, as we will explore the Python SDK for connecting apps and managing agentic logic.
❗ AI Concepts: A general awareness of Generative AI, Prompt Engineering, and LLMs (Large Language Models) will help you move faster through the material.
❗ Azure Subscription: A Microsoft Azure account (Free Tier or a Foundry Hub trial) is required to follow along with the architectural walkthroughs.
❗ No AI-102 Required: While this is the successor to the AI-102 exam, you do not need to be AI-102 certified to take and benefit from this course.
❗ An "Agent-First" Mindset: A willingness to learn new architectural patterns beyond traditional chatbots, focusing on autonomous reasoning and multi-agent workflows.
Description
"This course contains the use of artificial intelligence."
Master the2026 Agent-First Economy with the ultimate, production-focused guide to passing theMicrosoft Certified: Azure AI App and Agent Developer Associate (AI-103) exam.
The transition fromAI-102 (Azure AI Engineer) toAI-103 represents a major industry shift. Enterprise demand has evolved from simple Q&A chatbots toautonomous Multi-Agent Systems, multi-step reasoning orchestrators, and enterprise-grade RAG applications. This comprehensive 80+ lecture course provides the full architectural blueprint and practical execution needed to pass the exam and build production-ready AI solutions.
The 20 Architectural Practicals
Foundry Core Mechanics
✨Practical 1: Auditing RBAC: Simulating Identity Compromise & Least Privilege
✨Practical 2: Scripting Cost Alerts: Automating AI Compute Budget Monitoring
Generative AI App Development
✨Practical 3: Dynamic Few-Shot Prompting: Injecting Context via Python SDK
✨Practical 4: Multi-Modal Pipelines: Processing Text & Images for AI Analysis
✨Practical 5: Whisper API: Automating Meeting Transcription & Summarization
Advanced RAG & Retrieval
✨Practical 6: Custom Chunking: Optimizing PDF Layouts for Vector Search
✨Practical 7: Benchmarking Hybrid Search: Vector vs. BM25 Performance
✨Practical 8: Validation & Grounding: Implementing Source Citation Layers
Orchestration & Workflow Logic
✨Practical 9: Logic Apps: Orchestrating AI Inference via External Events
✨Practical 10: Session Memory: Stateless APIs vs. Stateful Context
✨Practical 11: Semantic Kernel: Creating Plugins for Live Web Searches
Agentic Systems & Architecture
✨Practical 12: Agent Tool Calling: Mapping REST APIs to JSON Schemas
✨Practical 13: Manager-Worker Pattern: Architecting Multi-Agent Delegation
✨Practical 14: Human-in-the-Loop: Adding Approval Checkpoints to Workflows
✨Practical 15: Containerizing Agents: Deploying to Azure Container Apps
Responsible AI & Performance Evaluation
✨Practical 16: Content Filters: Testing Toxicity & Jailbreak Protection
✨Practical 17: Latency Tracing: Monitoring Token/Sec & Response Metrics
✨Practical 18: Evaluating Grounded Ness: Automating Scores in AI Studio
✨Practical 19: Bias Mitigation: Comparative Prompting to Minimize Bias
✨Practical 20: Blue-Green Deployment: Zero-Downtime AI Agent Transitions
Comprehensive Curriculum Breakdown
✨The AI-103 Paradigm Shift: Navigating the upgrade from AI-102, setting up Azure Free Tier & Microsoft Foundry Hubs, and presenting agentic solutions to executive stakeholders.
✨Microsoft Foundry Core Mechanics: Unified workspace operations, multi-model deployments (GPT-5, Llama, Phi-4), RBAC security auditing, tokens/rate limits, and automated cost management scripts.
✨Generative AI App Development: Prompt engineering (Few-Shot, Chain-of-Thought), Python SDK integration, Text-to-SQL interfaces, multi-modal processing, and Whisper API transcription.
✨Implementing RAG with Azure AI Search: Solving knowledge cutoffs using custom PDF chunking, vector embeddings (text-embedding-3-large), hybrid search benchmarking, and grounding citation layers.
✨Multi-Step Reasoning Workflows: Orchestrating Logic Apps, managing context memory, parallel processing, and building custom Semantic Kernel plugins and planners.
✨Building Production-Ready AI Agents: Autonomous task decomposition, tool calling via REST/JSON, Manager-Worker multi-agent patterns, human-in-the-loop approvals, and containerization on Azure Container Apps.
✨Responsible AI & Performance Evaluation: Applying the Microsoft Responsible AI Standard, content safety toxicity filters, jailbreak detection, latency tracing, grounded ness evaluations, and blue-green deployments.
✨Certification Prep & Practice Exams: Granular domain reviews, capstone projects, stakeholder role-play presentations, and full-length simulated practice exams.
Who Is This Course For?
✨Azure AI Engineers (AI-102 certified): Looking to upgrade their certifications and master agentic workflows for the 2026 AI-103 exam.
✨Software Developers & Cloud Engineers: Aiming to move beyond basic API calls to build production-level agentic applications using Microsoft's complete AI stack.
✨Solution Architects & AI Leads: Designing enterprise-scale Generative AI architectures, RAG pipelines, and governance frameworks on Azure AI Foundry.
✨AI-103 Certification Candidates: Looking for a practical, objective-aligned curriculum packed with real-world scenarios, hands-on practicals, and mock exams.
Prerequisites
✨ Basic understanding of Azure cloud services (subscriptions, resource groups).
✨ Foundational knowledge of Python or REST APIs (useful for practical labs).
✨ Basic Generative AI concepts is helpful but not required.
Who this course is for
⭐ Existing Azure AI Engineers (AI-102): Professionals who want to upgrade their skills and certification to the new AI-103 Agent Developer standard for 2026.
⭐ Generative AI Developers: Software engineers who want to move beyond simple "Chat" prompts and build Autonomous Agents that take action and call APIs.
⭐ Solution Architects: Leaders designing enterprise-scale Retrieval-Augmented Generation (RAG) systems and high-accuracy vector search solutions.
⭐ Cloud Professionals: IT specialists looking to master Azure AI Foundry, Microsoft's unified workspace for the full AI project lifecycle.
Homepage
Code:
https://www.udemy.com/course/ai-103-azure-ai-agent-developerCode:
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