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The AI-103 Azure AI App and Agent Developer Associate certification is Microsoft’s new AI-focused certification path for developers and AI engineers, replacing the AI-102 Azure AI Engineer Associate certification, which is scheduled to retire on June 30, 2026. Microsoft’s official retirement information confirms that the Microsoft Certified: Azure AI Engineer Associate certification will retire on that date, while the AI-103 study guide identifies the new exam as Developing AI Apps and Agents on Azure, focused on Azure AI solutions that use Microsoft Foundry.

To prepare efficiently for this updated certification, candidates can use the complete preparation guide with the latest Passcert AI-103 dumps, which contain all exam objectives and real questions with accurate answers for focused practice. These updated materials help candidates understand generative AI applications, agentic workflows, Microsoft Foundry services, multimodal AI, retrieval and grounding pipelines, responsible AI controls, and production-ready agent deployment, giving them a practical path to practice for success and pass the AI-103 exam with confidence.

What Is the AI-103 Developing AI Apps and Agents on Azure Exam?

The AI-103: Developing AI Apps and Agents on Azure exam validates the skills required to build, manage, and deploy AI applications and intelligent agents on Microsoft Azure. According to Microsoft’s AI-103 study guide, candidates are Azure AI engineers who build, manage, and deploy agents and AI solutions using Microsoft Foundry, and they should have experience developing apps with Python plus familiarity with general AI, generative AI, and Azure services.

This certification is designed for developers, AI engineers, and professionals who want to create modern AI apps that go beyond basic AI service integration. The exam focuses on generative AI, agent design, multistep reasoning workflows, retrieval-augmented generation, tool integration, multi-agent orchestration, multimodal understanding, responsible AI, and operational monitoring. It is a strong option for candidates who want to demonstrate hands-on capability in building production-ready AI applications and agentic systems.

Why AI-103 Replaces AI-102 in the New Azure AI Certification Path

The retirement of AI-102 reflects Microsoft’s shift from traditional Azure AI engineering toward modern generative AI and agent-based development. The older AI-102 certification focused heavily on building AI solutions with Azure AI services, while AI-103 expands the role to include Microsoft Foundry, generative AI applications, agent workflows, multimodal processing, memory and tool integration, responsible AI instrumentation, and production observability.

Microsoft also lists AI-103T00: Develop AI apps and agents on Azure as the replacement course for AI-102T00: Develop AI solutions in Azure, which reinforces the transition from AI-102 learning content to the AI-103 app-and-agent development path. For candidates planning their certification roadmap, this means AI-103 is the more current path for validating Azure AI app and agent development skills.

Ideal Candidates for the AI-103 Certification

The AI-103 Azure AI App and Agent Developer Associate certification is intended for candidates who build and maintain AI solutions using Azure AI services and Microsoft Foundry. In this role, candidates collaborate with business stakeholders, solution architects, data scientists, DevOps engineers, and cloud security engineers to design, implement, deploy, secure, and monitor AI systems.

Candidates should have practical experience with:

• Azure AI services and Microsoft Foundry
• Python application development
• General AI and generative AI concepts
• Large language models and small language models
• Agent workflows and tool integration
• Retrieval-augmented generation and grounding
• Vector search and indexing methods
• Multimodal AI for image, video, text, and speech scenarios
• Responsible AI, safety filters, and content moderation
• Monitoring, tracing, evaluation, and governance for AI systems

The exam is especially relevant for developers and AI engineers who want to prove that they can build secure, reliable, and scalable AI applications and agents in real Azure environments.

Detailed AI-103 Exam Objectives and Skill Areas

The AI-103 exam measures skills across five major areas: planning and managing Azure AI solutions, implementing generative AI and agentic systems, building computer vision solutions, implementing text and speech analysis, and creating information extraction pipelines.

Plan and manage an Azure AI solution (25–30%)

Choose the appropriate Foundry services for generative AI and agents

• Choose an appropriate model for each task, including large language models (LLMs), small language models, multimodal models, and Foundry Tools
• Choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing
• Choose an appropriate method for retrieval and indexing
• Choose appropriate memory, tool, and knowledge integration services for agent solutions

Set up AI solutions in Foundry

• Design Azure infrastructure for AI apps and agent-based solutions
• Choose appropriate deployment options
• Configure model and agent deployments
• Integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines

Manage, monitor, and secure AI systems

• Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads
• Monitor model performance, drift, safety events, and grounding quality
• Monitor data ingestion quality, search index health, and relevance performance
• Configure security, including managed identity, private networking, keyless credentials, and role policies

Implement responsible AI across generative AI and agentic systems

• Configure safety filters, guardrails, risk detection, and content moderation
• Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling
• Implement auditing through trace logging, provenance metadata, and approval workflows
• Govern agent behavior with oversight modes, constraints, and tool-access controls

Implement generative AI and agentic solutions (30–35%)

Build generative applications by using Foundry

• Deploy and consume LLMs, small models, code models, and multimodal models
• Implement retrieval-augmented generation (RAG) in an application
• Design workflows, tool-augmented flows, and multistep reasoning pipelines
• Evaluate models and apps, including detecting fabrications, relevance, quality, and safety
• Integrate generative workflows into applications by using Foundry SDKs and connectors
• Configure an application to connect to a Foundry project

Build agents by using Foundry

• Define agent roles, goals, conversation-tracking approach, and tool schemas
• Build agents that integrate retrieval, function-calling, and conversation memory
• Integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions
• Implement orchestrated multi-agent solutions
• Build autonomous or semiautonomous workflows with safeguards and approval flow controls
• Integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis

Optimize and operationalize generative AI systems

• Tune generation behavior, such as prompt engineering and adjusting model parameters
• Implement model reflection, chain-of-thought evaluations, and self-critique loops
• Set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns
• Orchestrate multiple models, flows, or hybrid LLM and rules engines

Implement computer vision solutions (10–15%)

Design and implement image- and video-generation solutions

• Implement a solution that generates images from text prompts and reference media
• Implement a solution that generates videos from text prompts and reference media
• Configure image-editing workflows, including inpainting, mask‑based edits, and prompt‑driven modifications
• Implement workflows to edit generated videos
• Select and apply appropriate generation and editing controls provided by the platform

Design and implement multimodal understanding workflows

• Build a solution that analyzes visual context by using multimodal models
• Configure apps to produce concise or detailed captions for single or multiple images
• Implement a solution that enables question‑answering grounded in visual evidence
• Configure generation of alt‑text and extended image descriptions aligned to accessibility guidelines
• Implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics
• Implement video analysis workflows to process and interpret video segments
• Configure single‑task and pro‑mode Content Understanding pipelines
• Implement solutions that identify objects, components, or regions within images or video

Implement responsible AI for multimodal content

• Implement filters to classify unsafe or disallowed visual content
• Detect and mitigate indirect prompt injection by using embedded text in images
• Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content

Implement text analysis solutions (10–15%)

Apply language model text analysis

• Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools
• Configure detection of sentiment, tone, safety issues, and sensitive content
• Build solutions that translate text by using Azure Translator in Foundry Tools or LLM‑powered translation flows
• Customize language model outputs for domain tasks, such as compliance summarization and domain extraction

Implement speech solutions

• Implement workflows to convert speech to text and text to speech for agentic interactions
• Integrate speech as an agent modality, including custom speech models
• Enable multimodal reasoning from audio inputs
• Translate speech into other languages by using language models and Foundry Tools

Implement information extraction solutions (10–15%)

Build retrieval and grounding pipelines

• Ingest and index content, such as documents, images, audio, and video
• Configure semantic search, hybrid search, and vector search for grounding
• Implement enrichment by using custom or built-in skills for text, images, and layout
• Configure RAG ingestion flow, including documents and using optical character recognition (OCR)
• Connect retrieval pipelines directly to workflows and agent tools

Extract content from documents

• Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction
• Produce clean, grounded representations to use with agents and RAG by using Content Understanding
• Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding

Difference Between AI-102 and AI-103

Overall, AI-103 is not just a simple exam code update from AI-102. It represents a major shift in Microsoft’s Azure AI certification direction. While AI-102 focused on traditional Azure AI service implementation, AI-103 places much stronger emphasis on generative AI, Microsoft Foundry, intelligent agents, multimodal workflows, RAG, responsible AI, and production-ready AI systems. Candidates who originally planned to take AI-102 should now move to AI-103 to keep their certification path aligned with Microsoft’s latest AI development requirements.

Best Study Tips to Prepare for the AI-103 Exam

To prepare effectively for the AI-103 Developing AI Apps and Agents on Azure exam, candidates should use a balanced study plan that combines objective review, practical Azure AI experience, and real-question practice.

• Focus on the highest-weighted domains first Start with Implement Generative AI and Agentic Solutions and Plan and Manage an Azure AI Solution. Prioritize model selection, Foundry services, RAG, agent design, tool integration, responsible AI, security, monitoring, and deployment.
• Use the Latest Passcert AI-103 Dumps for Practice The latest Passcert AI-103 dumps help you practice real exam-style questions, spot key topics faster, and build confidence.
• Build Practical Skills with AI Apps and Agents AI-103 tests hands-on skills. Practice connecting apps to Foundry, deploying and using models, building RAG, setting up tools and memory, and monitoring agents.
• Review Weak Areas After Each Practice Test Review wrong answers, group them by topic, and spend extra time on your weakest areas until they are clear.
• Understand Responsible AI, Security, and Monitoring Review safety filters, guardrails, content moderation, evaluation, trace logs, identity, private networking, role policies, and tool controls.
• Practice Scenario-Based Questions Regularly Many AI-103 questions use real scenarios. Practice choosing the right Azure AI service, Foundry tool, model, workflow, or fix for each requirement.

Final Thoughts: Prepare for the New AI-103 Azure AI App and Agent Developer Certification

The AI-103 Developing AI Apps and Agents on Azure exam is a major update for Microsoft Azure AI certification candidates. With AI-102 Azure AI Engineer Associate retiring on June 30, 2026, AI-103 introduces a more modern path centered on Microsoft Foundry, generative AI applications, intelligent agents, multimodal AI, grounding, responsible AI, and production operations.

By preparing with the latest Passcert AI-103 dumps, reviewing the full exam objectives, and building hands-on knowledge of AI apps, agents, RAG workflows, multimodal solutions, text and speech systems, and information extraction pipelines, candidates can improve their readiness and build the skills needed for success in the new Azure AI certification path.