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Microsoft Exam Syllabus

AI-102 syllabus, skills measured, and exam topics

The AI-102 exam measures Plan and manage an Azure AI solution, Implement generative AI solutions, and Implement an agentic solution. Use this page to review the current official syllabus, major domains, and source links before exam day.

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Plan and manage an Azure AI solution 20–25%
Implement generative AI solutions 15–20%
Implement an agentic solution 5–10%
Implement computer vision solutions 10–15%
Implement natural language processing solutions 15–20%
Implement knowledge mining and information extraction solutions 15–20%
Plan and manage an Azure AI solution 20-25%
Implement an agentic solution 5-10%
Implement computer vision solutions 10-15%

What to know before you study

These sections explain the role, audience, and exam framing behind the outline.

Purpose of this document

  • This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
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  • Request accommodations: If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation.
  • Take a free Practice Assessment: Test your skills with practice questions to help you prepare for the exam.

Updates to the exam

  • Our exams are updated periodically to reflect skills that are required to perform a role. We have included two versions of the Skills Measured objectives depending on when you are taking the exam.
  • We always update the English language version of the exam first. Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized exams as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
  • The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
  • Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Audience profile

  • As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.
  • Your responsibilities include participating in all phases of AI solutions development, including:
  • Requirements definition and design
  • Development
  • Deployment
  • Integration
  • Maintenance
  • Performance tuning
  • Monitoring
  • You work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to:
  • Build complete and secure end-to-end AI solutions.
  • Integrate AI capabilities in other applications and solutions.

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Plan and manage an Azure AI solution (20-25%)

  • Select the appropriate service for a generative AI solution
  • Select the appropriate service for a computer vision solution
  • Select the appropriate service for a natural language processing solution
  • Select the appropriate service for a speech solution
  • Select the appropriate service for an information extraction solution
  • Select the appropriate service for a knowledge mining solution
  • Plan for a solution that meets Responsible AI principles
  • Create an Azure AI resource
  • Choose the appropriate AI models for your solution
  • Deploy AI models using the appropriate deployment options
  • Install and utilize the appropriate SDKs and APIs
  • Determine a default endpoint for a service

Implement generative AI solutions (15–20%)

  • Plan and prepare for a generative AI solution
  • Deploy a hub, project, and necessary resources with Microsoft Foundry
  • Deploy the appropriate generative AI model for your use case
  • Implement a prompt flow solution
  • Implement a RAG pattern by grounding a model in your data
  • Evaluate models and flows
  • Integrate your project into an application with Microsoft Foundry SDK
  • Utilize prompt templates in your generative AI solution
  • Provision an Azure OpenAI in Foundry Models resource
  • Select and deploy an Azure OpenAI model
  • Submit prompts to generate code and natural language responses
  • Use the DALL-E model to generate images

Implement an agentic solution (5-10%)

  • Understand the role and use cases of an agent
  • Configure the necessary resources to build an agent
  • Create an agent with the Microsoft Foundry Agent Service
  • Implement complex agents with Microsoft Agent Framework
  • Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities
  • Test, optimize and deploy an agent

Implement computer vision solutions (10-15%)

  • Select visual features to meet image processing requirements
  • Detect objects in images and generate image tags
  • Include image analysis features in an image processing request
  • Interpret image processing responses
  • Extract text from images using Azure Vision in Foundry Tools
  • Convert handwritten text using Azure Vision in Foundry Tools
  • Choose between image classification and object detection models
  • Label images
  • Train a custom image model, including image classification and object detection
  • Evaluate custom vision model metrics
  • Publish a custom vision model
  • Consume a custom vision model