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AI-103 Study Guide: Azure AI Apps and Agents Developer Associate Exam Preparation

Your comprehensive guide to the Associate-level AI-103 certification, built around Microsoft Foundry. Covers planning and managing Azure AI solutions, generative AI and agents, computer vision, text analysis, and information extraction for developers and AI engineers.

120 Min
Exam Duration
40-60
Questions
700/1000
Passing Score
Associate
Certification Level

TL;DR - Quick Summary

Target Audience

Developers and AI engineers building generative AI apps, agents, and intelligent solutions on Azure with Microsoft Foundry.

Exam Format

40-60 questions in 120 minutes; passing score is 700/1000. Five sections, no formal prerequisite.

Key Focus Areas

Planning and securing Azure AI solutions, generative AI and agents in Foundry, computer vision, text analysis, and information extraction with Content Understanding.

Developer Focused

Hands-on with the Foundry portal and SDK, prompt engineering, retrieval augmented generation, and the Azure AI Agent Service. Python or C# experience helps.

Coming from AI-102?

If you already know the AI-102 Azure AI Engineer track, here is how AI-103 differs:

  • AI-103 is agent-first. A full section is dedicated to building, testing, and orchestrating agents with the Azure AI Agent Service in Microsoft Foundry.
  • Foundry is the unified surface. Implementation centres on the Foundry portal and SDK rather than provisioning individual services in isolation.
  • Information extraction has expanded. Azure Content Understanding covers documents, images, audio, and video in standard-mode and pro-mode pipelines.
  • Retrieval augmented generation is core. Grounding models with Azure AI Search and evaluating responses for groundedness are tested throughout the generative AI section.

What is the Microsoft AI-103 Exam?

The Microsoft AI-103 exam, officially titled Developing AI Apps and Agents on Azure, leads to the Azure AI Apps and Agents Developer Associate certification. It is aimed at developers who design, build, and operate AI solutions on Azure using Microsoft Foundry, the unified platform for deploying models, building agents, and managing AI workloads.

The certification validates your ability to plan and manage an Azure AI solution end to end, then implement generative AI and agentic solutions, computer vision, text analysis, and information extraction. It is a practical, code-aware exam, so you are expected to understand the Foundry SDK, prompt engineering, retrieval augmented generation, and the security and responsible AI controls that surround a production AI service.

Who Should Take the AI-103 Exam?

Azure AI Developers

Developers building generative AI features into applications who want a credential that proves hands-on Foundry and SDK skills.

AI Engineers

Engineers designing agentic systems, orchestrating tools, and grounding models with retrieval augmented generation in production.

Full-Stack Developers

Application developers extending existing products with vision, language, and document-processing capabilities on Azure.

AI-901 Graduates

Candidates who have passed AI-901 Azure AI Fundamentals and want an Associate-level developer credential as the natural next step.

Exam Objectives Breakdown

AI-103 covers five sections. The generative AI and agentic solutions section carries the most weight, reflecting the exam's focus on building and orchestrating agents in Microsoft Foundry.

1
Section 1

Plan and manage an Azure AI solution

25-30%

Select and provision Azure AI resources

  • Choose the right Azure AI services for a given requirement
  • Create single-service and multi-service resources and pick a region
  • Plan container deployments for disconnected or edge scenarios

Secure and govern Azure AI services

  • Manage authentication with keys, Microsoft Entra ID, and managed identities
  • Apply network security, private endpoints, and customer-managed keys
  • Configure responsible AI with Azure AI Content Safety and content filters

Monitor and manage solutions in production

  • Monitor cost, usage, metrics, and diagnostic logs
  • Manage keys, quotas, and rate limits across environments
  • Plan for high availability and continuity of an AI solution
Majority of exam weight
2
Section 2

Implement generative AI and agentic solutions

30-35%

Build generative AI solutions with Microsoft Foundry

  • Deploy and consume models in the Foundry portal and with the Foundry SDK
  • Engineer system and user prompts and configure parameters such as temperature
  • Ground responses with retrieval augmented generation using Azure AI Search

Build and orchestrate agents with the Azure AI Agent Service

  • Create, test, and deploy single agents in Microsoft Foundry
  • Add tools and function calling, including connected and custom tools
  • Orchestrate multi-agent solutions that hand off tasks between agents

Evaluate and safeguard generative solutions

  • Apply content filters and safety guardrails to model responses
  • Evaluate responses for groundedness, relevance, and coherence
  • Manage responsible AI risks across the generative AI lifecycle
3
Section 3

Implement computer vision solutions

10-15%

Analyse images and video with Azure AI Vision

  • Generate captions, tags, and object detection from images
  • Read printed and handwritten text with the OCR Read capability
  • Interpret and generate visual content with multimodal models in Foundry

Build custom vision models and apply face analysis

  • Train custom image classification and object detection models
  • Use face detection and analysis within responsible AI policy
  • Integrate vision capabilities into a client application
4
Section 4

Implement text analysis solutions

10-15%

Analyse text with Azure AI Language

  • Extract key phrases, named entities, and linked entities
  • Detect language, analyse sentiment, and mine opinions
  • Detect and redact personally identifiable information
  • Summarise documents and conversations

Build custom language models

  • Train custom text classification models
  • Build custom named entity recognition models
  • Implement conversational language understanding and question answering
5
Section 5

Implement information extraction solutions

10-15%

Extract data with Azure Content Understanding

  • Use prebuilt analysers for invoices, receipts, and identity documents
  • Build custom analysers for bespoke document layouts
  • Extract information from images, audio, and video in standard-mode and pro-mode

Combine extraction with knowledge retrieval

  • Index extracted content with Azure AI Search
  • Feed extracted fields into downstream agents and applications
  • Build a lightweight application with information extraction capabilities

Microsoft Foundry Technologies to Know

AI-103 is built around Microsoft Foundry and the Azure AI service family. Focus your hands-on study on these four areas.

Microsoft Foundry Portal & SDK

The unified workspace for deploying models, engineering prompts, building agents, and running client applications. The Foundry SDK is the primary developer surface.

Azure AI Agent Service

Create, test, and deploy agents in Foundry, then add tools and function calling and orchestrate multi-agent solutions that hand off work between agents.

Azure AI Search

The retrieval layer for grounding generative models. Used for retrieval augmented generation so responses stay accurate and anchored to your own data.

Azure Content Understanding

Foundry Tools service for extracting structured information from documents, images, audio, and video using standard-mode and pro-mode pipelines.

Recommended Study Resources

Official Microsoft Resources

  • Microsoft Learn: AI-103 Learning Path
  • Microsoft Foundry Documentation
  • Azure AI Agent Service Documentation
  • Azure AI Search and RAG Documentation
  • Azure Content Understanding Documentation

Examinotion Practice

Built for the Foundry-centric AI-103 exam

  • 280+ exam-style questions across all five sections
  • Section weights matched to the official objectives
  • Full-length mock exams with detailed explanations
  • Dedicated agents, RAG, and Content Understanding questions

Start Practising

280+ AI-103 practice questions are ready for you today.

28-Day Roadmap

Structured Study Plan for AI-103

This four-week plan tracks the official section weights, dedicating the most time to building and orchestrating agents in Microsoft Foundry.

W1
Section 1 • 25-30%

Plan & Manage Azure AI

Provisioning and security

Days 1-3

Selecting and provisioning Azure AI resources, regions, and container deployments.

Days 4-7

Authentication with Microsoft Entra ID and managed identities, network security, content safety, and monitoring.

W2
Section 2 • 30-35%

Generative AI & Agents

The core of the exam

Days 8-11

Deploying models in Foundry, prompt engineering, parameters, and retrieval augmented generation with Azure AI Search.

Days 12-14

Building agents with the Azure AI Agent Service, tools and function calling, multi-agent orchestration, and response evaluation.

W3
Sections 3-4 • 20-30%

Vision & Text Analysis

Azure AI Vision and Language

Days 15-18

Image analysis, OCR, multimodal vision, custom vision models, and face analysis under responsible AI policy.

Days 19-21

Key phrases, entities, sentiment, PII redaction, summarisation, and custom language models.

W4
Section 5 + Review

Extraction & Mock Exams

Content Understanding and revision

Days 22-25

Azure Content Understanding for documents, images, audio, and video in standard-mode and pro-mode, plus indexing with Azure AI Search.

Days 26-28

Full-length practice exams, targeted review of weak areas, and final exam preparation.

Why most time on Week 2? Generative AI and agentic solutions account for 30 to 35 percent of the scored questions, the single largest section. Spend extra hands-on time in the Foundry portal building and testing agents, since that is where the exam concentrates its weight.

Frequently Asked Questions

What is the AI-103 exam?
AI-103 (Azure AI Apps and Agents Developer Associate), officially titled Developing AI Apps and Agents on Azure, is an Associate-level Microsoft certification for developers who build AI solutions on Azure. It validates your ability to plan and manage an Azure AI solution and to implement generative AI and agentic solutions, computer vision, text analysis, and information extraction using Microsoft Foundry. The exam runs for 120 minutes with a passing score of 700 out of 1000.
What is the difference between AI-102 and AI-103?
AI-102 (Azure AI Engineer Associate) and AI-103 are both Associate-level Azure AI developer certifications, but AI-103 is the newer, agent-focused exam. AI-103 centres on building generative AI and agentic solutions with Microsoft Foundry, alongside computer vision, text analysis, and information extraction with Azure Content Understanding. AI-102 has historically focused on implementing Azure AI services across vision, language, speech, and knowledge mining. If your work centres on building AI apps and agents on the current Microsoft Foundry platform, AI-103 is the better fit.
Are there prerequisites for the AI-103 exam?
There is no formal prerequisite, but AI-103 is an Associate-level developer exam, so Microsoft expects hands-on experience. You should be comfortable developing with Azure AI services, calling REST APIs and SDKs, and writing code in a supported language such as Python or C#. Familiarity with the Microsoft Foundry portal, prompt engineering, and retrieval augmented generation will make the implementation topics considerably easier.
What is the passing score for AI-103?
The passing score for AI-103 is 700 out of 1000, consistent with other Microsoft role-based certifications. This equates to roughly 70 percent, though scoring is scaled according to the difficulty weighting of each question across the five exam sections.
How long should I study for the AI-103 exam?
Most candidates with practical Azure development experience prepare in 40 to 60 hours over four to six weeks. If you are new to Microsoft Foundry or to building agents, allow closer to six weeks and spend extra time in the portal and SDK. Pairing full-length practice exams with the official Microsoft Learn modules is the fastest route to a confident pass.
Which programming languages and tools does AI-103 cover?
AI-103 is built around Microsoft Foundry, the unified platform for deploying models, building agents, and running client applications. The implementation topics centre on the Foundry portal and the Foundry SDK, with code typically shown in Python or C#. You should also understand Azure AI Search for grounding and retrieval augmented generation, Azure AI Vision and Azure AI Language for analysis, and Azure Content Understanding for information extraction.
Can I use Microsoft Learn during the AI-103 exam?
Yes. Microsoft provides access to Microsoft Learn documentation during role-based exams such as AI-103, so you can confirm a specific parameter or method signature mid-exam. The clock keeps running while you search, however, so you should know the material well and treat Learn access as a safety net rather than a substitute for preparation.

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