Microsoft AI-103 Real Exam Dumps [June 2026 Update]

AI-103 Dumps 2026 – Prepare for Microsoft Azure AI Apps and Agents Developer the Right Way

The Microsoft AI-103 exam — Developing AI Apps and Agents on Azure — is Microsoft’s new AI developer certification launched in beta on April 21, 2026. It replaces the AI-102 Azure AI Engineer Associate (retiring June 30, 2026) and earns the Microsoft Certified: Azure AI Apps and Agents Developer Associate credential. The exam lasts 120 minutes, targets developers who build and deploy AI applications and agents using Azure AI Foundry, and covers five domains: Plan and Manage Azure AI Solutions (25-30%), Implement Generative AI and Agentic Solutions (30-35%, highest), Implement Computer Vision Solutions (10-15%), Implement NLP Solutions (10-15%), and Implement Knowledge Mining and Document Intelligence Solutions (10-15%).

At Cert Empire, we help you prepare with updated AI-103 exam materials covering all five exam domains with the developer-level technical depth Microsoft’s certification requires. Candidates building the complete Microsoft AI certification stack can also explore our Microsoft AI-300 MLOps Engineer Associate exam dumps, Microsoft AI-200 Azure AI Cloud Developer exam dumps, and Microsoft AI-901 Azure AI Fundamentals exam dumps.

Understand What the AI-103 Exam Is Really Testing

AI-103 represents Microsoft’s recognition that the most important Azure AI skill in 2026 is building agents and generative AI applications — not configuring individual AI services in isolation. The AI-102 it replaces was primarily a “service configuration” exam: here is Azure AI Vision, here is Azure AI Language, here is how you connect them. AI-103 shifts focus to Azure AI Foundry as the unified development platform and tests how you build complete, production-ready AI applications and agents on top of it.

This means the exam rewards developers who have built actual AI agents — not those who have configured AI services. Candidates who prepared for AI-102 without building with Azure AI Foundry, Semantic Kernel, or agent frameworks will find AI-103 more demanding than expected in the generative AI and agentic solutions domain.

Key Takeaway: AI-103 is in beta (opened April 21, 2026, GA expected June 2026). It replaces AI-102, which retires June 30, 2026. If you have completed 80%+ of AI-102 preparation, finish that path before the retirement date. If you are starting fresh in 2026, prepare for AI-103 directly.

The Microsoft AI Certification Stack in 2026

AI-103 is the core AI builder exam — for developers who build AI capabilities (agents, RAG pipelines, AI services integration). AI-200 is for developers who build the application infrastructure that hosts those capabilities.

The Official AI-103 Domain Weights

Domain 2 is the highest weighted at 30-35% and covers the newest, most specifically AI-103 content — Azure AI Foundry, agents, RAG, and responsible AI evaluation. Domain 1 (planning and management) at 25-30% covers foundation-wide Azure AI infrastructure. Together these two domains account for 55-65% of the exam.

What the AI-103 Exam Covers

Domain 1: Plan and Manage an Azure AI Solution (25-30%)

This domain covers how to provision, configure, secure, and monitor Azure AI resources.

Azure AI Foundry is the unified development platform for building AI applications and agents in Azure. It provides access to foundation models (Azure OpenAI, Llama, Mistral, and others through the model catalog), a development environment for building and testing AI workflows, connections to Azure AI services, deployment infrastructure for AI applications, and evaluation and monitoring capabilities.

Resource provisioning and management covers creating and configuring Azure AI Foundry projects, Azure OpenAI service resources, Azure AI Search instances, and Azure AI services endpoints. Understanding which resources are required for which AI application patterns and how they connect is specifically tested.

Security and access control covers using Azure RBAC to control access to AI resources, managed identity for service-to-service authentication (AI applications authenticating to Azure OpenAI, Azure AI Search, and storage without credential storage), network security (private endpoints, virtual network integration), and content filtering configuration for responsible AI.

Monitoring and logging covers configuring Application Insights integration for AI application telemetry, logging AI service usage for audit and compliance, and setting up alerts for service availability and performance.

Domain 2: Implement Generative AI and Agentic Solutions (30-35%)

This is the highest-weighted domain and the one that most specifically tests AI-103 content beyond what AI-102 covered.

Azure AI Foundry agent development covers building AI agents using the Azure AI Foundry Agent Service. An agent is an AI system that can plan, use tools, and take multi-step actions to complete tasks. Agent development includes: defining the agent’s model (which foundation model powers it), defining tools the agent can use (code interpreter, file search, custom functions, Azure AI Search), managing agent memory and conversation context, and deploying agents for production use.

RAG (Retrieval Augmented Generation) implementation covers building complete RAG pipelines in Azure AI Foundry: creating an Azure AI Search index for the knowledge base, chunking and indexing source documents, configuring vector search (hybrid search combining semantic and keyword retrieval), connecting the Azure AI Search index to an Azure OpenAI model deployment through Azure AI Foundry, and evaluating RAG response quality for groundedness and relevance.

Semantic Kernel is Microsoft’s open-source AI orchestration SDK that AI-103 tests for building complex AI workflows. Semantic Kernel connects to AI models, manages conversation history and memory, and enables multi-step AI processes with tool calling and function composition. The exam tests Semantic Kernel concepts including plugins (collections of functions the AI can call), planners (AI-driven plan generation for achieving goals), and kernel setup with Azure OpenAI connections.

Responsible AI and evaluation covers applying responsible AI principles in generative AI applications: content filter configuration (blocking harmful content categories), groundedness evaluation (checking that AI responses accurately reflect source documents), and using Azure AI Foundry’s evaluation tools to run automated quality assessments.

Prompt engineering and management covers crafting effective system prompts, few-shot examples, and instruction templates; managing prompt versions as lifecycle artifacts; and testing prompt variations for quality and safety.

Domain 3: Implement Computer Vision Solutions (10-15%)

Computer vision covers AI capabilities that analyze images and video. Key Azure AI services tested:

Azure AI Vision provides image analysis (describing image content, detecting objects, reading text), face detection, optical character recognition (OCR), and video analysis capabilities. The exam tests how to call Azure AI Vision APIs, interpret the response schema, and select the appropriate feature for described image analysis requirements.

Azure AI Custom Vision enables training custom image classification models (which category does this image belong to?) and custom object detection models (where in this image are specific objects?) using labeled training data. The exam tests the training workflow and evaluation metrics.

Azure AI Video Indexer provides AI analysis of video content including transcription, speaker identification, scene detection, and visual search.

Domain 4: Implement Text Analysis Solutions (10-15%)

Natural language processing covers AI capabilities that analyze and process human language.

Azure AI Language provides: Sentiment Analysis (positive/neutral/negative sentiment at document and sentence level), Named Entity Recognition (identifying people, places, organizations, dates, and quantities in text), PII Detection (identifying personally identifiable information for privacy compliance), Key Phrase Extraction, Text Classification, and Conversational Language Understanding (CLU — building intent recognition and entity extraction models for chatbots and virtual assistants).

Azure AI Translator provides machine translation between hundreds of languages, with support for custom translation models trained on domain-specific terminology.

Azure AI Speech provides speech-to-text transcription, text-to-speech synthesis, speaker recognition, and pronunciation assessment. The exam tests when each speech service is appropriate and how to configure custom speech models.

Domain 5: Implement Knowledge Mining and Document Intelligence (10-15%)

Azure AI Search is the managed search service that enables full-text search, semantic search, and vector search over enterprise data. The exam tests indexer configuration (connecting to data sources like Azure Blob Storage, Azure SQL, or SharePoint), skillset design (chaining cognitive skills like OCR, entity extraction, and key phrase extraction during indexing), and search API usage.

Azure AI Document Intelligence (formerly Form Recognizer) analyzes document layouts and extracts structured information. Prebuilt models handle common document types (invoices, receipts, identity documents, tax forms) without training. Custom models can be trained for organization-specific document formats.

Why Candidates Choose Cert Empire for AI-103 Preparation

Cert Empire’s AI-103 preparation is built around the actual Azure AI Foundry developer skills the new exam tests.

✔ Azure AI Foundry and agent development at full exam depth 

Domain 2 (Implement Generative AI and Agentic Solutions, 30-35%) receives proportionally more practice questions matching its highest exam weight. Azure AI Foundry Agent Service, RAG pipeline implementation, Semantic Kernel orchestration, and responsible AI evaluation are all covered at the implementation depth the exam requires.

✔ AI-102 to AI-103 transition guidance included 

The shift from AI-102’s service-by-service approach to AI-103’s Azure Foundry-unified platform is explicitly explained, so candidates know exactly what is new, what carries over, and where to focus additional preparation effort to close the gap between the two exams.

✔ All five domain weights confirmed and proportionally covered 

Domain 1 (Plan and Manage, 25-30%), Domain 2 (GenAI and Agents, 30-35%), Domain 3 (Computer Vision, 10-15%), Domain 4 (NLP, 10-15%), and Domain 5 (Knowledge Mining, 10-15%) each receive appropriate coverage — ensuring no domain is either over-prepared or neglected relative to its exam weight.

✔ Practice under real exam conditions with the Cert Empire Exam Simulator 

The Cert Empire exam simulator replicates the 120-minute AI-103 Microsoft exam format with scenario-based developer questions across all five domains. It tracks your performance by domain after every session, identifies whether your gaps are in Foundry architecture, agent development, RAG pipelines, or individual AI services, and builds the Azure AI developer judgment that first-attempt passes require. For a new beta exam where question patterns are still emerging, simulator practice under time pressure is especially valuable.

✔ Instant access, 90-day free updates, and 24/7 support

After purchase, receive immediate access to all AI-103 materials. Your purchase includes 90 days of free updates — critical for a beta exam transitioning to GA in June 2026 where content stabilizes and new official practice questions are released. Our 24/7 customer support team is available at any time.

✔ Backed by a full money-back guarantee

Cert Empire backs all AI-103 preparation materials with a complete money-back guarantee. Explore our complete Microsoft certification catalog.

FAQS

What is the Microsoft AI-103 exam? 

The AI-103 is the Microsoft exam for Developing AI Apps and Agents on Azure, earning the Microsoft Certified: Azure AI Apps and Agents Developer Associate credential. It launched in beta April 21, 2026 and replaces AI-102 (retiring June 30, 2026). It covers planning Azure AI solutions, building generative AI apps and agents with Azure AI Foundry, computer vision, NLP, and document intelligence. Passing score is 700/1000, duration is 120 minutes.

What is Azure AI Foundry? 

Azure AI Foundry (formerly Azure AI Studio) is Microsoft’s unified platform for building, testing, deploying, and monitoring AI applications and agents. It provides access to foundation models, a development environment for AI workflows, connections to Azure AI services, deployment infrastructure, and evaluation tools. AI-103 treats Azure AI Foundry as the primary development platform, which is a significant shift from AI-102’s individual-service approach.

What is the difference between AI-103 and AI-102? 

AI-102 focused on configuring individual Azure AI services in isolation. AI-103 focuses on building complete AI applications and agents using Azure AI Foundry as the unified platform. AI-103 adds substantial new content on agentic AI development (Azure AI Foundry Agent Service, Semantic Kernel), RAG pipeline implementation, and responsible AI evaluation. AI-102 retires June 30, 2026.