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AI-103 Study Guide

A curated learning path for the AI-103 exam: the best documentation, videos, blog posts and tutorials for every exam objective.

Microsoft Certified: Azure AI Apps and Agents Developer Associate

 

Before You Start Studying

Before diving into the study sections, review the prerequisites and recommended background knowledge below to understand what will help you succeed.

 

Recommended Exam Path

AI-103 is an associate-level exam and the core credential for AI developers on Azure. Passing it earns the certification Microsoft Certified: Azure AI Apps and Agents Developer Associate.

The exam is centered on Microsoft Foundry: building generative AI apps, agents, and multimodal solutions with Foundry Models, Foundry Agent Service, and Foundry Tools. Agentic development is the largest skill area.

If you are new to AI on Azure, AI-901: Azure AI Fundamentals is the recommended starting point. It introduces the same Foundry-centered world at a conceptual level.

AI-901 helps you build:

  • Foundational understanding of AI workloads and Microsoft Foundry
  • First hands-on exposure to deploying models and testing agents in the Foundry portal
  • The vocabulary that AI-103 assumes you already know

If you already build AI solutions on Azure with Python, you can go directly to AI-103.

 

Prerequisites

There are no strict prerequisites to start AI-103 preparation, but the exam assumes real development experience.

You should be comfortable with:

  • Developing applications in Python
  • Navigating the Azure portal and the Microsoft Foundry portal
  • Core generative AI concepts such as prompts, tokens, embeddings, and RAG
  • Calling REST APIs and working with SDKs

 

Recommended Background Knowledge

AI-103 is organized around five skill areas. Agentic and generative development carry the most weight, and planning and management questions run through everything.

Plan and Manage an Azure AI Solution (25-30% of the exam)

  • Choosing the right model (LLM, small model, multimodal) and the right Foundry service for a task
  • Deployment types and options, quotas, rate limits, scaling, and cost management
  • Integrating Foundry projects with CI/CD pipelines
  • Monitoring model performance, drift, safety events, and grounding quality
  • Security with managed identities, keyless authentication, private networking, and role policies
  • Responsible AI: content filters, guardrails, evaluators, trace logging, and agent oversight controls

Implement Generative AI and Agentic Solutions (30-35% of the exam)

  • Deploying and consuming models through the Foundry SDK and connecting apps to a Foundry project
  • Implementing RAG, workflows, tool-augmented flows, and multistep reasoning pipelines
  • Building agents with tools, knowledge stores, retrieval, function calling, and conversation memory
  • Orchestrating multi-agent solutions and adding safeguards and approval flows
  • Evaluating models, apps, and agents for fabrications, relevance, quality, and safety
  • Tuning generation behavior with prompt engineering, model parameters, and self-critique loops
  • Observability: tracing, token analytics, safety signals, and latency breakdowns

Implement Computer Vision Solutions (10-15% of the exam)

  • Generating images and videos from text prompts and reference media
  • Image editing workflows including inpainting and mask-based edits
  • Multimodal understanding: captions, visual question answering, and accessibility descriptions
  • Azure Content Understanding for visual characteristics, video analysis, and single-task versus pro-mode pipelines
  • Responsible AI for visual content, including filters and indirect prompt injection through images

Implement Text Analysis Solutions (10-15% of the exam)

  • LLM-based text analysis with generative prompting: entities, topics, summaries, and structured JSON output
  • Detecting sentiment, tone, safety issues, and sensitive content
  • Translation with Azure Translator in Foundry Tools or LLM-powered translation flows
  • Speech to text, text to speech, custom speech models, and speech translation for agent interactions

Implement Information Extraction Solutions (10-15% of the exam)

  • Ingesting and indexing documents, images, audio, and video for grounding
  • Semantic, hybrid, and vector search configuration in Azure AI Search
  • RAG ingestion flows including OCR, enrichment skills, and connecting retrieval to agent tools
  • Azure Content Understanding analyzers that produce structured or markdown output for downstream reasoning

Note that older standalone services such as Custom Vision, Video Indexer, custom question answering, and conversational language understanding do not appear in the April 2026 objectives: video analysis is tested through multimodal models and Content Understanding, curated Q&A through agent knowledge tools and RAG, and custom classification through language-model prompting. Budget your study time accordingly.

 

Step-by-Step Study Guide

 

Step 1: Review the Official Study Guide

What to do:

  • Open the official AI-103 study guide
  • Read the skills measured sections (current version dated April 16, 2026)
  • Note any topics that are new to you
  • Use it as your checklist throughout your prep

Link to The Official Study Guide

 

Step 2: Schedule Your Exam

What to do:

  • Choose a date that gives you enough time for study and practice
  • Schedule the exam through the official Microsoft certification page
  • Put the date on your calendar and plan backwards

Recommended timing:

  • If you already build with Microsoft Foundry weekly: 2 to 4 weeks
  • If you know Azure but are new to Foundry and agents: 4 to 8 weeks
  • If you are new to AI development: 8 to 12 weeks

Certification and Exam Details Page

 

Step 3: Go Through the Official Learning Paths

AI-103 has four official learning paths, and they map directly to the four modules you will find in CertiAce practice.

What to do:

  • Start with the generative AI apps path to build the platform foundation
  • Continue with the agents path, which covers the largest skill area
  • Complete the natural language and visual data paths to round out the services coverage
  • Take notes on concepts you cannot explain in simple terms

Develop Generative AI Apps in Azure

Develop AI Agents on Azure

Develop Natural Language Solutions in Azure

Extract Insights from Visual Data on Azure

 

Step 4: Get Hands-On with Microsoft Foundry

AI-103 rewards real development experience with Microsoft Foundry. The exam expects you to recognize configurations, trade-offs, and failure modes that you only meet by building.

You should aim to get experience with:

  • Deploying models with different deployment types and observing quota and rate-limit behavior
  • Building an agent with tools (file search, Azure AI Search, OpenAPI, MCP) and a memory store
  • Implementing RAG against an Azure AI Search index, including agentic retrieval
  • Running evaluations for groundedness, relevance, and safety, and reading traces in Application Insights
  • Creating an Azure Content Understanding analyzer for a document schema
  • Calling speech to text, text to speech, and translation from the SDK
  • Generating and editing images with the image APIs, including mask-based edits

Create an Azure Free Account

Microsoft Foundry Documentation

 

Step 5: Deep-Dive the Service Documentation

What to do:

  • Read the Foundry documentation for agents, deployment types, and observability end to end
  • Skim the documentation for each Foundry Tool the exam names, focusing on when you would choose it
  • Prefer the current Foundry-era documentation over older study material that covers retired services

Foundry Agent Service Documentation

Azure AI Search Documentation

Azure Content Understanding

Azure Speech

Azure Language

Azure Translator

 

Step 6: Benchmark Your Knowledge

What to do:

  • Use CertiAce to benchmark your readiness
  • Practice exam-style questions across all four modules, including the case studies
  • Review explanations carefully, especially for wrong answers
  • Return to Microsoft Learn and hands-on practice for weak topics

Recommended target:

  • Aim for consistent performance, not one lucky high score
  • If a topic is unstable, return to learning + hands-on
  • Pay close attention to questions that hinge on requirements such as data residency, cost, least privilege, or avoiding reserved capacity

Note that the official Microsoft practice assessment for AI-103 is not available yet. Microsoft usually publishes practice assessments within about 8 weeks of an exam leaving beta, so check the practice assessments page for updates.

CertiAce AI-103 Exam Practice

Microsoft Practice Assessments

 

Step 7: Take the Exam

The day before:

  • Review your weak topics only
  • Revisit deployment types, agent tool choices, and the evaluator names
  • Avoid learning brand new topics

On exam day:

  • Read questions carefully and identify what they are truly asking
  • Eliminate wrong options first
  • Watch for wording that implies constraints such as data residency, cost, least privilege, or minimal development effort
  • A score of 700 or greater is required to pass
  • The certification renews annually through a free online assessment on Microsoft Learn

 

Additional Learning Resources

 

Microsoft Official Resources

 

Microsoft Product Documentation

 

YouTube

 

Community

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