Welcome to the AI-103: Develop AI Apps and Agents on Azure Exam Prep Hub!
Welcome to the one-stop hub with information for preparing for the AI-103: Develop AI Apps and Agents on Azure certification exam. The content for this exam helps you to demonstrate that “you have conceptual knowledge of AI solutions in Azure and the foundational technical skills to work with them”. You will also need “knowledge of Python coding syntax and programming techniques, and you should be familiar with Azure resources”.
Upon successful completion of the exam, you earn the Microsoft Certified: Azure AI Apps and Agents Developer Associate certification.
This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the AI-103 exam and making use of as many of the resources available as possible.
Audience profile (from Microsoft’s site)
As a candidate for this Microsoft Certification, you’re an Azure AI engineer who builds, manages, and deploys agents and AI solutions that take advantage of Microsoft Foundry.
For this exam, you should have experience developing apps by using Python, and you need to be familiar with the capabilities of general AI, generative AI, and Azure services.
Your responsibilities include:
- Planning and managing Azure AI solutions.
- Implementing generative AI and agentic solutions.
- Implementing computer vision solutions.
- Implementing text analysis solutions.
- Implementing information extraction solutions.
In this role, you collaborate with business stakeholders, solution architects, data scientists, DevOps engineers, and cloud security engineers to design, implement, and maintain AI solutions.
Skills at a glance (as specified in the official study guide)
- Plan and manage an Azure AI solution (25–30%)
- Implement generative AI and agentic solutions (30–35%)
- Implement computer vision solutions (10–15%)
- Implement text analysis solutions (10–15%)
- Implement information extraction solutions (10–15%)
Topic-by-Topic Exam Content
[click a topic link to access the content and practice questions for that topic]
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
- Govern agent behavior with oversight modes, constraints, and tool-access controls
- 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
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
AI-103: Develop AI Apps and Agents on Azure – Practice Exams
- AI-103: Practice Exam #1 (30 questions with answers)
- AI-103: Practice Exam #2 (30 questions with answers)
- AI-103: Practice Exam #3 (30 questions with answers)
- AI-103: Practice Exam #4 (30 questions with answers)
Important AI-103 Resources
- Link to the free, comprehensive, self-paced course – Develop AI apps and agents on Azure – on Microsoft Learn
The course has 4 modules:
(1) Develop generative AI apps in Azure
https://learn.microsoft.com/en-us/training/paths/develop-generative-ai-apps/
(2) Develop AI agents on Azure
https://learn.microsoft.com/en-us/training/paths/develop-ai-agents-azure/
(3) Develop natural language solutions in Azure
https://learn.microsoft.com/en-us/training/paths/develop-language-solutions-azure-ai/
(4) Extract insights from visual data on Azure
https://learn.microsoft.com/en-us/training/paths/insight-visual-data/
- Link to certification page and study guide:
– Link to the certification page: Microsoft Certified: Azure AI Apps and Agents Developer Associate (beta)
– Link to the study guide: Study Guide for the Exam AI-103: Develop AI Apps and Agents on Azure - YouTube resources:
– AI-103 Exam Review: AI-103 Exam Review (Beta)
– AI-103 Exam & Study Guide: Microsoft’s New AI-103 Agent Developer Cert: Exam Guide + Study Plan (2026)
– AI-103 Content: What is Artificial Intelligence? 5 Core AI Capabilities Every Azure Developer Must Know | AI-103
- A course on Udemy that you might be interested in: AI-103: Azure AI App and Agent Developer – Complete Course
Good luck to you on your data journey!
The Data Community 