AI-102 to AI-103: Azure AI Apps and Agents Developer Exam Guide

Quick Overview

• The AI 103 certification validates skills in developing AI applications and agents using Microsoft Foundry and Azure services.
• The AI-103 exam covers five domains, with generative AI and agentic solutions carrying the highest weightage of 30-35%.
• The exam lasts 120 minutes, requires a passing score of 700 out of 1,000, and costs $165 in the United States, with regional fees varying by country.
• Preparation can take 2-4 weeks for experienced Azure AI developers or 8-12 weeks or longer for beginners, depending on their Python and Azure knowledge.
• This guide covers AI-102 vs AI-103 changes, exam format and fees, registration steps, syllabus, preparation resources, and study timelines.

Build on the AI-103 syllabus by learning how to design, develop, and implement AI agents through practical projects. Explore Applied Agentic AI Certification Course. 

What is the AI 103 certification?

AI 103 certification is related to Microsoft Certified: Azure AI Apps and Agents Developer Associate credential. It proves the abilities to design and manage AI apps and agents on Microsoft Azure.

The exam will test the knowledge of Microsoft Foundry, generative AI apps, AI agents, computer vision, text analytics, and information extraction. Candidates need to know Python, APIs, Azure services, and basics of AI apps development.

AI 103 certification is especially helpful for those who are interested not only in the usage of AI but also in the creation of AI solutions that integrate models, data sources, and third-party tools.

AI-102 vs AI-103: What has changed?

AI-102 concentrated on the implementation and management of Azure AI solutions through various services like language, vision, knowledge mining. AI 103 certification pays more attention to the development of generative AI apps and agentic flows.

Comparison point	AI-102	AI-103
Certification title	Azure AI Engineer Associate	Azure AI Apps and Agents Developer Associate
Exam status	Retired on June 30, 2026	Current exam
Primary focus	Building and managing Azure AI solutions	Developing AI applications and agents
Generative AI and agents	Included in the earlier objectives	Dedicated domain with 30-35% weighting
Other skill areas	Vision, language, and knowledge mining	AI solution management, vision, text analysis, and information extraction
Preparation	AI-102 study resources	Updated AI-103 study guide and learning paths

Also Read: 15 Best Azure Certifications 2026: Which one to Choose?

What is the AI 103 certification exam format?

The AI-103 exam tests practical knowledge of how to develop AI applications and agents on Azure.

Exam detail	Information
Certification	Azure AI Apps and Agents Developer Associate
Exam code	AI-103
Exam duration	120 minutes
Passing score	700 out of 1,000
Exam fee	USD165; regional pricing may vary
Delivery	Proctored exam through Pearson VUE
Main focus	Generative AI, agents, and Azure AI solutions

The cutoff mark is 700 out of Microsoft's total of 1,000 points. When planning to take the exam, it is necessary to check the duration and the methods of delivery on the official exam page, since Microsoft may change its policies and arrangements.

Also Read: Microsoft Azure Certifications Cost in 2026

How much does AI 103 certification cost in different countries?

The cost of the AI 103 certification exam depends on the country or region. Based on regional pricing, there can be taxes involved as well, thus the total cost can be different from the price mentioned in the table below.

Country or region	Indicative exam fee
United States	USD165
Canada	USD140
Australia	AUD140
United Kingdom	£106
Germany	€126
France	€126
India	₹4,865
Japan	¥20,300
Brazil	USD99
Philippines	USD83

Deepen your knowledge of generative AI and prompt engineering to support your preparation for AI-103 topics such as AI applications and generative AI solutions by enrolling in Generative AI and Prompt Engineering Course.

How do you register for AI-103?

Candidates may apply for the AIm103 certification exam via Microsoft Learn and schedule it through Pearson VUE. Depending on their location, candidates may take advantage of either the online proctored option or test centre examinations.

Follow these steps to register:

1. Open the official exam page: Visit the AI-103 exam page.
2. Select Schedule exam: Choose the Pearson VUE scheduling option.
3. Sign in to Microsoft Learn: Use a personal Microsoft account and check that the name on the certification profile matches the required identification.
4. Choose the delivery method: Select an available test centre or online proctoring option.
5. Pick a date and time: Check the available appointments and select a suitable slot.
6. Complete payment: Confirm the regional exam fee, pay, and save the booking confirmation.

It is recommended by Microsoft that people register using a personal Microsoft account since certification records associated with a work or school account could become inaccessible if the candidate leaves that organisation.

Also Read: Microsoft Azure Salary in 2026

What is the AI 103 certification exam syllabus?

The syllabus for the AI 103 certification exam includes five skill domains which are concerned with developing, deploying, securing and managing AI applications on Azure.  Candidates should understand Python-based development, Microsoft Foundry, generative AI, AI agents, and the services used for processing text, images and documents.

AI-103 exam syllabus and domain weightings

Exam domain	Weight
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%

Also Read: Top Cloud Certifications

What topics are covered in each AI-103 exam domain?

1. Plan and manage an Azure AI solution

Learn how to select suitable models and Foundry services, configure deployments, manage costs, monitor performance, and secure AI applications. Responsible AI practices, access controls, and application monitoring are also important.

2. Implement generative AI and agentic solutions

This is the largest exam domain. Study how to build generative AI applications, implement retrieval-augmented generation (RAG), connect agents to APIs and knowledge sources, manage conversation history, and evaluate responses. You should also understand agent orchestration, safeguards, tracing, and performance monitoring.

3. Implement computer vision solutions

Focus on image and video generation, image editing, multimodal understanding, visual question answering, and extracting useful information from visual content. Learn how to apply appropriate safety controls to generated and analysed content.

4. Implement text analysis solutions

Prepare for tasks involving text summarisation, sentiment analysis, entity extraction, translation, and structured outputs. Speech processing, including speech-to-text and text-to-speech, is also part of this domain.

5. Implement information extraction solutions

Understand how to process documents using optical character recognition (OCR), analyse document layouts, extract structured fields, and create retrieval pipelines. Study vector search, hybrid search, and how retrieved information supports RAG applications.

Which AI-103 exam domains should you prioritise?

Begin with generative AI and agentic solutions since they make up 30-35% of the exam, then look at study planning and the management of Azure AI solutions, which carry a weight of 25-30%.

The two domains combined make up between 55-65%of the exam. After these, study computer vision, text analysis, and information extraction. Do not skip the smaller domains since each of them has an impact on the final score.

Looking to expand your AI and data analytics skills beyond AI 103 certification? Explore the PG Certificate in Data Analytics and Applied AI to build practical skills in data analysis and applied AI. 

How to prepare for the AI 103 certification exam?

A well-organized study plan can make it easier to study by concentrating on the official objectives, making use of Microsoft's learning materials, and practicing the development of working AI applications rather than depending solely on theory.

1. Review the official AI-103 exam objectives

Read the official study guide and make a checklist of all five skill domains. Mark the topics you already understand and those that need more practice. Use the domain weightings to decide where to spend your time.

2. Follow the official Microsoft Learn AI-103 study resources

Microsoft offers learning paths that cover applications of generative AI, AI agents, natural language solutions, and the processing of visual data.

Begin by using the Azure learning materials on developing AI apps and agents. Focus on one topic at a time and carry out the practical exercises as much as you can. 

3. Build hands-on Azure AI projects

Practical experience helps to apply concepts to real development tasks. Here's are some projects which are suitable for beginners, try building these projects:

• RAG application: Create a question-answering app that retrieves information from a set of documents.
• AI agent: Build an agent that calls an API or searches a knowledge source to complete a task.
• Document extraction tool: Extract names, dates, and other fields from sample documents.
• Image analysis application: Use a multimodal model to describe the image and respond to questions regarding its content.

4. Take the official AI-103 practice assessment

Use the practice assessment that is available via Microsoft's AI Skills Navigator to test your understanding. Look over the answers that you got wrong, go back to the relevant documentation and then repeat the assessment after studying the areas where you struggled. You have the option of using Microsoft's exam sandbox in order to get used to the testing interface.

Also Read: Top Business Analyst Certification

How long does it take to prepare for AI-103?

Preparation time depends on your Python skills, Azure experience, and familiarity with generative AI. The timelines below are estimates for planning purposes, not official Microsoft recommendations.

Experienced Azure AI developers: 2-4 weeks

Developers who already create AI applications using Azure should concentrate on the revised exam objectives, agent workflows, Microsoft Foundry, and the practice assessments. They must take extra time to study any areas that are unfamiliar to them.

Azure developers new to AI agents: 4-8 weeks

Developers who know about Azure but have little experience in generative AI should study RAG, model integration, agent tools, memory, and evaluation. Working on practical projects can help build their confidence.

AI developers new to Azure: 6-10 weeks

People who have experience in AI development but who know only a limited amount about Azure should, together with the exam objectives, study the relevant Azure services, deployments, identity management, monitoring, and security practices.

Beginners with limited Python and AI experience: 8-12 weeks or longer

Beginners may need additional time to learn Python, understand APIs, explore cloud computing fundamentals, and build basic AI applications before moving to advanced topics.

Set a realistic weekly schedule and move on to practice assessments when you can explain the key concepts and complete basic projects without relying entirely on step-by-step instructions.

Also Read: Is Microsoft Azure Certification Worth It?

Conclusion

The AI 103 certification offers a way for developers to demonstrate their skills in building generative AI applications and agents on Azure. Compared with AI-102, the exam places greater emphasis on agentic solutions, Microsoft Foundry, and practical AI development.

Start by reviewing the official syllabus, prioritising the highest-weighted domains, and practising with hands-on projects. With a clear study plan and regular practice, candidates can prepare more effectively for the exam and build skills relevant to real-world Azure AI solutions.

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