AI-103 Preparation Details
Preparing for the AI-103 Developing AI Apps and Agents on Azure certification exam? Start here with a complete, objective-wise AI-103 study guide designed to help you pass faster.
This guide brings together official Microsoft documentation, key concepts, and curated resources for every AI-103 exam objective, making it ideal for both beginners and last-minute revision.
Looking for the best AI-103 preparation resources in one place? This page covers everything you need to get exam-ready with confidence.
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AI-103 AI Leader Materials
| Udemy Course | Azure AI App & Agent Developer Associate Exam Prep |
| Udemy Practice Test | Azure AI App & Agent Developer Practice Exams |
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 LLMs, small language models, multimodal models, and Foundry Tools
Microsoft Foundry Models overview
Foundry Models sold directly by Azure
Foundry Models from partners and community
Choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing
What is Microsoft Foundry Agent Service?
Vector Search Overview – Azure AI Search
Choose an appropriate method for retrieval and indexing
Retrieval augmented generation (RAG) and indexes in Microsoft Foundry
RAG and generative AI – Azure AI Search
Agentic Retrieval Overview – Azure AI Search
Choose appropriate memory, tool, and knowledge integration services for agent solutions
Agent tools overview for Microsoft Foundry Agent Service
What is Memory? – Microsoft Foundry
Connect Agents to Foundry IQ Knowledge Bases
Set up AI solutions in Foundry
Design Azure infrastructure for AI apps and agent-based solutions
Microsoft Foundry architecture
Baseline Microsoft Foundry Chat Reference Architecture
High availability and resiliency for Microsoft Foundry projects and Agent Services
Choose appropriate deployment options
Deployment overview for Azure AI Foundry Models
Understanding deployment types in Microsoft Foundry Models
Configure model and agent deployments
Deploy Microsoft Foundry Models in the Foundry portal
What is Microsoft Foundry Agent Service?
Integrate Foundry projects with CI/CD pipelines
Baseline Microsoft Foundry Chat Reference Architecture
High availability and resiliency for Microsoft Foundry projects and Agent Services
Manage, monitor, and secure AI systems
Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads
Manage and increase quotas for resources – Microsoft Foundry
Microsoft Foundry Models quotas and limits
Quotas and limits for Microsoft Foundry Agent Service
Monitor model performance, drift, safety events, and grounding quality
Observability in Generative AI – Microsoft Foundry
Monitor Model Deployments in Microsoft Foundry Models
Monitor agents with the Agent Monitoring Dashboard
Monitor data ingestion quality, search index health, and relevance performance
RAG and generative AI – Azure AI Search
What is Microsoft Foundry Control Plane?
Observability in Generative AI – Microsoft Foundry
Configure security, including managed identity, private networking, keyless credentials, and role policies
Role-based access control for Microsoft Foundry
Azure security baseline for Microsoft Foundry
Govern Azure platform services (PaaS) for AI
Implement responsible AI across generative AI and agentic systems
Configure safety filters, guardrails, risk detection, and content moderation
Guardrails and controls overview in Microsoft Foundry
Content filtering for Microsoft Foundry Models
How to use Risks & Safety monitoring in Microsoft Foundry
Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling
Risk and Safety Evaluators for Generative AI – Microsoft Foundry
Microsoft Foundry risk and safety evaluations Transparency Note
Responsible AI in Azure Workloads – Azure Well-Architected Framework
Implement auditing through trace logging, provenance metadata, and approval workflows
Agent tracing in Microsoft Foundry
Set Up Tracing for AI Agents in Microsoft Foundry
Configure tracing for AI agent frameworks – Microsoft Foundry
Govern agent behavior with oversight modes, constraints, and tool-access controls
Transparency Note for Azure Agent Service – Microsoft Foundry
Governing Agent Identities – Microsoft Entra ID Governance
Governance and security for AI agents across the organization
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
Microsoft Foundry Models overview
How to deploy and inference a managed compute deployment
Foundry Models sold directly by Azure
Implement retrieval-augmented generation (RAG) in an application
Retrieval augmented generation (RAG) and indexes in Microsoft Foundry
Build a custom knowledge retrieval (RAG) app with the Microsoft Foundry SDK
Build a RAG solution using Azure Content Understanding in Foundry Tools
Design workflows, tool-augmented flows, and multistep reasoning pipelines
Build a workflow in Microsoft Foundry
Prompt flow in Microsoft Foundry portal
Microsoft Agent Framework overview
Evaluate models and apps, including detecting fabrications, relevance, quality, and safety
Retrieval-Augmented Generation (RAG) Evaluators for Generative AI
Risk and Safety Evaluators for Generative AI – Microsoft Foundry
Observability in Generative AI – Microsoft Foundry
Integrate generative workflows into applications by using Foundry SDKs and connectors
Get started with Microsoft Foundry SDKs and Endpoints
Integrate Microsoft Foundry with your applications
Azure AI Foundry SDK client libraries
Configure an application to connect to a Foundry project
Create a project – Microsoft Foundry
Integrate Microsoft Foundry with your applications
Build agents by using Foundry
Define agent roles, goals, conversation-tracking approach, and tool schemas
What is Microsoft Foundry Agent Service?
Understand agent runtime components in Foundry Agent Service
Agent tools overview for Microsoft Foundry Agent Service
Build agents that integrate retrieval, function-calling, and conversation memory
Build with agents, conversations, and responses in Foundry Agent Service
What is Memory? – Microsoft Foundry
Microsoft Agent Framework multi-turn conversations
Integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions
Agent tools overview for Microsoft Foundry Agent Service
Connect Agents to Foundry IQ Knowledge Bases
Add tools to custom agents – Microsoft Copilot Studio
Implement orchestrated multi-agent solutions
Build a workflow in Microsoft Foundry
Microsoft Agent Framework agent types
Baseline Microsoft Foundry Chat Reference Architecture
Build autonomous or semiautonomous workflows with safeguards and approval flow controls
Transparency Note for Azure Agent Service – Microsoft Foundry
How to Use Task Adherence for Your Agentic Workflows
Process to build agents across your organization with Microsoft Foundry and Copilot Studio
Integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis
Agent Evaluators for Generative AI – Microsoft Foundry
Monitor agents with the Agent Monitoring Dashboard
Agent Evaluation with the Microsoft Foundry SDK
Optimize and operationalize generative AI systems
Tune generation behavior, such as prompt engineering and adjusting model parameters
Prompt engineering techniques – Microsoft Foundry
Getting started with customizing a large language model (LLM)
Prompt flow in Microsoft Foundry portal
Implement model reflection, chain-of-thought evaluations, and self-critique loops
How to use reasoning models with Microsoft Foundry Models
Retrieval-Augmented Generation (RAG) Evaluators for Generative AI
Agent Evaluators for Generative AI – Microsoft Foundry
Set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns
Observability in Generative AI – Microsoft Foundry
Set Up Tracing for AI Agents in Microsoft Foundry
Monitor Model Deployments in Microsoft Foundry Models
Orchestrate multiple models, flows, or hybrid LLM and rules engines
Build a workflow in Microsoft Foundry
Microsoft Agent Framework overview
Prompt flow in Microsoft Foundry portal
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
How to Use Image Generation Models from OpenAI – Microsoft Foundry
Quickstart: Generate images with Azure OpenAI in Microsoft Foundry Models
Use the image generation tool in Foundry Agent Service
Implement a solution that generates videos from text prompts and reference media
Video generation with Sora 2 – Microsoft Foundry
Quickstart: Generate video with Sora – Azure OpenAI
Configure image-editing workflows, including inpainting, mask-based edits, and prompt-driven modifications
How to use image generation models – Azure OpenAI
How to Use Image Generation Models from OpenAI – Microsoft Foundry
Implement workflows to edit generated videos
Video generation with Sora 2 – Microsoft Foundry
Quickstart: Generate video with Sora – Azure OpenAI
Select and apply appropriate generation and editing controls provided by the platform
Generate images with AI – Training
Design and implement multimodal understanding workflows
Build a solution that analyzes visual context by using multimodal models
How to use vision-enabled chat models – Microsoft Foundry
What is Image Analysis? – Foundry Tools
Configure apps to produce concise or detailed captions for single or multiple images
Image captions – Image Analysis 4.0 – Foundry Tools
Multimodal Search Concepts and Guidance – Azure AI Search
Implement a solution that enables question-answering grounded in visual evidence
How to use vision-enabled chat models – Microsoft Foundry
Transparency Note and use cases for Image Analysis
Configure generation of alt-text and extended image descriptions aligned to accessibility guidelines
Overview: Generate alt text of images with Image Analysis
Image captions – Image Analysis 4.0 – Foundry Tools
Implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics
What is Azure Content Understanding in Foundry Tools?
Create an Azure AI Content Understanding single-file task in the Foundry portal
Implement video analysis workflows to process and interpret video segments
Azure Content Understanding in Foundry Tools video overview
What is Azure Content Understanding in Foundry Tools?
Configure single-task and pro-mode Content Understanding pipelines
Azure Content Understanding standard and pro modes
Create Content Understanding Standard and Pro tasks in the Foundry portal
Implement solutions that identify objects, components, or regions within images or video
Object detection using Image Analysis 4.0 – Foundry Tools
Azure Content Understanding in Foundry Tools video overview
Implement responsible AI for multimodal content
Implement filters to classify unsafe or disallowed visual content
What is Azure AI Content Safety?
Content filtering for Microsoft Foundry Models
Content Safety in Microsoft Foundry portal overview
Detect and mitigate indirect prompt injection by using embedded text in images
Content filtering in Azure AI Foundry portal
Guardrails and controls overview in Microsoft Foundry
Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content
What is Azure AI Content Safety?
Azure Content Understanding in Foundry Tools video overview
Risk and Safety Evaluators for Generative AI – Microsoft Foundry
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
What is Azure Language in Foundry Tools
What is summarization? – Foundry Tools
How to use structured outputs with Azure OpenAI in Microsoft Foundry Models
Configure detection of sentiment, tone, safety issues, and sensitive content
What is Azure Language in Foundry Tools
What is Azure AI Content Safety?
Content filtering for Microsoft Foundry Models
Build solutions that translate text by using Azure Translator in Foundry Tools or LLM-powered translation flows
What is Azure Translator in Foundry Tools?
What is Azure Text translation in Foundry Tools?
Azure Translator in Foundry Tools 2025-10-01-preview reference
Customize language model outputs for domain tasks, such as compliance summarization and domain extraction
Create a custom analyzer with Azure Content Understanding in Foundry Tools using REST APIs
Azure Content Understanding in Foundry Tools prebuilt analyzers
Fine-tune models with Microsoft Foundry
Implement speech solutions
Implement workflows to convert speech to text and text to speech for agentic interactions
Speech to Text Overview – Speech Service – Foundry Tools
Text to speech overview – Speech service – Foundry Tools
Choose an Azure Speech Recognition and Generation Technology
Integrate speech as an agent modality, including custom speech models
Voice Live API Overview – Foundry Tools
Custom speech overview – Speech service – Foundry Tools
Train a custom speech model – Speech service – Foundry Tools
Enable multimodal reasoning from audio inputs
Use the LLM-speech API – Speech service – Foundry Tools
Choose an Azure Speech Recognition and Generation Technology
How to use vision-enabled chat models – Microsoft Foundry
Translate speech into other languages by using language models and Foundry Tools
Speech translation overview – Speech service – Foundry Tools
Use the LLM-speech API – Speech service – Foundry Tools
What is Azure Translator in Foundry Tools?
Implement information extraction solutions (10–15%)
Build retrieval and grounding pipelines
Ingest and index content, such as documents, images, audio, and video
AI Enrichment Overview – Azure AI Search
Multimodal Search Concepts and Guidance – Azure AI Search
Integrated vector embedding in Azure AI Search
Configure semantic search, hybrid search, and vector search for grounding
Vector Search Overview – Azure AI Search
Hybrid search using vectors and full text in Azure AI Search
Quickstart: Multimodal Search in the Azure portal – Azure AI Search
Implement enrichment by using custom or built-in skills for text, images, and layout
Skills Reference – Azure AI Search
Skillset Concepts – Azure AI Search
Custom Skill Interface – Azure AI Search
Configure RAG ingestion flow, including documents and using OCR
Tutorial: Skillsets – Azure AI Search
Document Layout Skill – Azure AI Search
Part 2: Build a custom knowledge retrieval (RAG) app with the Foundry SDK
Connect retrieval pipelines directly to workflows and agent tools
Agentic Retrieval Overview – Azure AI Search
Tutorial: Build an Agentic Retrieval Solution – Azure AI Search
Connect an Azure AI Search index to Foundry agents
Extract content from documents
Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction
Azure Content Understanding Skill – Azure AI Search
Document Layout Skill – Azure AI Search
Use AI Enrichment With Image and Text Processing – Azure Architecture Center
Produce clean, grounded representations to use with agents and RAG by using Content Understanding
Build a RAG solution with Azure Content Understanding in Foundry Tools
Azure Content Understanding in Foundry Tools Document Overview
What is Foundry IQ? – Microsoft Foundry
Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding
Create a custom analyzer with Azure Content Understanding in Foundry Tools using REST APIs
Document Analysis: Extract Structured Content with Azure Content Understanding in Foundry Tools
Azure Content Understanding in Foundry Tools prebuilt analyzers
This brings us to the end of the AI-103 Developing AI Apps and Agents on Azure Study Guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!
In case you are preparing for other Azure certification exams, check out the Azure certification study guides for those exams.
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