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Study guide for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

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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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.

Note

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.

Note

Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Skills measured as of December 23, 2025

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.

As an Azure AI engineer, you have experience developing solutions that use languages such as:

  • Python

  • C#

You should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure. You should:

  • Understand the components that make up the Azure AI portfolio and the available data storage options.

  • Be able to apply responsible AI principles.

Skills at a glance

  • 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%)

Select the appropriate Microsoft Foundry Services

  • 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, create and deploy a Microsoft Foundry Service

  • 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

  • Integrate Microsoft Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline

  • Plan and implement a container deployment

Manage, monitor, and secure a Microsoft Foundry Service

  • Monitor an Azure AI resource

  • Manage costs for Microsoft Foundry Services

  • Manage and protect account keys

  • Manage authentication for a Microsoft Foundry Service resource

Implement AI solutions responsibly

  • Implement content moderation solutions

  • Configure responsible AI insights, including content safety

  • Implement responsible AI, including content filters and blocklists

  • Prevent harmful behavior, including prompt shields and harm detection

  • Design a responsible AI governance framework

Implement generative AI solutions (15–20%)

Build generative AI solutions with Microsoft Foundry

  • 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

Use Azure OpenAI in Foundry Models to generate content

  • 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

  • Integrate Azure OpenAI into your own application

  • Use large multimodal models in Azure OpenAI

Optimize and operationalize a generative AI solution

  • Configure parameters to control generative behavior

  • Configure model monitoring and diagnostic settings, including performance and resource consumption

  • Optimize and manage resources for deployment, including scalability and foundational model updates

  • Enable tracing and collect feedback

  • Implement model reflection

  • Deploy containers for use on local and edge devices

  • Implement orchestration of multiple generative AI models

  • Apply prompt engineering techniques to improve responses

  • Fine-tune a generative model

Implement an agentic solution (5-10%)

Create custom agents

  • 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%)

Analyze images

  • 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

Implement custom vision models

  • 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

  • Build a custom vision model code first

Analyze videos

  • Use Azure AI Video Indexer to extract insights from a video or live stream

  • Use Azure Vision in Foundry Tools Spatial Analysis to detect presence and movement of people in video

Implement natural language processing solutions (15–20%)

Analyze and translate text

  • Extract key phrases and entities

  • Determine sentiment of text

  • Detect the language used in text

  • Detect personally identifiable information (PII) in text

  • Translate text and documents by using the Azure Translator in Foundry Tools service

Process and translate speech

  • Integrate generative AI speaking capabilities in an application

  • Implement text-to-speech and speech-to-text using Azure Speech in Foundry Tools

  • Improve text-to-speech by using Speech Synthesis Markup Language (SSML)

  • Implement custom speech solutions with Azure Speech in Foundry Tools

  • Implement intent and keyword recognition with Azure Speech in Foundry Tools

  • Translate speech-to-speech and speech-to-text by using the Azure Speech in Foundry Tools service

Implement custom language models

  • Create intents, entities, and add utterances

  • Train, evaluate, deploy, and test a language understanding model

  • Optimize, backup, and recover language understanding model

  • Consume a language model from a client application

  • Create a custom question answering project

  • Add question-and-answer pairs and import sources for question answering

  • Train, test, and publish a knowledge base

  • Create a multi-turn conversation

  • Add alternate phrasing and chit-chat to a knowledge base

  • Export a knowledge base

  • Create a multi-language question answering solution

  • Implement custom translation, including training, improving, and publishing a custom model

Implement knowledge mining and information extraction solutions (15–20%)

Implement an Azure AI Search solution

  • Provision an Azure AI Search resource, create an index, and define a skillset

  • Create data sources and indexers

  • Implement custom skills and include them in a skillset

  • Create and run an indexer

  • Query an index, including syntax, sorting, filtering, and wildcards

  • Manage Knowledge Store projections, including file, object, and table projections

  • Implement semantic and vector store solutions

Implement an Azure Document Intelligence in Foundry Tools solution

  • Provision a Document Intelligence resource

  • Use prebuilt models to extract data from documents

  • Implement a custom document intelligence model

  • Train, test, and publish a custom document intelligence model

  • Create a composed document intelligence model

Extract information with Azure Content Understanding in Foundry Tools

  • Create an OCR pipeline to extract text from images and documents

  • Summarize, classify, and detect attributes of documents

  • Extract entities, tables, and images from documents

  • Process and ingest documents, images, videos, and audio with Azure Content Understanding in Foundry Tools

Study resources

We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.

Study resources Links to learning and documentation
Get trained Choose from self-paced learning paths and modules or take an instructor-led course
Find documentation Azure AI services
Azure AI Vision
Azure AI Video Indexer
Azure AI Language
Azure AI Speech
Azure AI Search
Azure OpenAI
Azure AI Document Intelligence
Ask a question Microsoft Q&A | Microsoft Docs
Get community support AI - Machine Learning - Microsoft Tech Community
AI - Machine Learning Blog - Microsoft Tech Community
Follow Microsoft Learn Microsoft Learn - Microsoft Tech Community
Find a video The AI Show
Browse other Microsoft Learn shows

Change log

The table below summarizes the changes between the current and previous version of the skills measured. The functional groups are in bold typeface followed by the objectives within each group. The table is a comparison between the previous and current version of the exam skills measured and the third column describes the extent of the changes.

Skill area prior to December 23, 2025 Skill area as of December 23, 2025 Change
Plan and manage an Azure AI solution Plan and manage an Azure AI solution No change
Select the appropriate Azure AI Foundry Services Select the appropriate Microsoft Foundry Services Minor
Plan, create and deploy an Azure AI Foundry Service Plan, create and deploy an Microsoft Foundry Service Minor
Manage, monitor, and secure an Azure AI Foundry Service Manage, monitor, and secure an Microsoft Foundry Service Minor
Implement generative AI solutions Implement generative AI solutions No change
Build generative AI solutions with Azure AI Foundry Build generative AI solutions with Microsoft Foundry Minor
Use Azure OpenAI in Foundry Models to generate content Use Azure OpenAI in Foundry Models to generate content Minor
Implement an agentic solution Implement an agentic solution No change
Create custom agents Create custom agents Minor
Implement computer vision solutions Implement computer vision solutions No change
Analyze images Analyze images Minor
Implement custom vision models Implement custom vision models Minor
Analyze videos Analyze videos Minor
Implement natural language processing solutions Implement natural language processing solutions No change
Analyze and translate text Analyze and translate text Minor
Implement knowledge mining and information extraction solutions Implement knowledge mining and information extraction solutions No change
Implement an Azure AI Document Intelligence solution Implement an Azure Document Intelligence in Foundry Tools solution Minor
Extract information with Azure AI Content Understanding Extract information with Azure Content Understanding in Foundry Tools Minor
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