Microsoft Azure AI Engineer Associate Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Plan and manage an Azure AI solution (23.35% of the exam)

Select the appropriate Azure AI 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

Summary: This section focuses on learning which Azure AI Foundry service is best suited for specific solution types such as generative AI, computer vision, natural language processing, speech, and knowledge mining. You will build a clear understanding of how Microsoft structures its AI offerings and when to use each to meet unique business needs.

The emphasis is on developing strong decision-making skills around service selection, ensuring you can pair solution requirements with the most capable AI service. By recognizing the intended scenarios of each offering, you will gain the ability to design architectures that are efficient, scalable, and well-positioned for growth.

Plan, create and deploy an Azure AI 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 Azure AI Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline
• Plan and implement a container deployment

Summary: This section ensures you can move from planning to full deployment of Azure AI services, with a sharp focus on Responsible AI principles. You will learn not only how to create and configure AI resources but also how to embed governance, ethics, and compliance into the overall plan.

Hands-on areas include deploying models with the most appropriate options, managing endpoints, and integrating AI services into modern CI/CD pipelines. The goal is to make AI deployment predictable, well-structured, and aligned to enterprise-grade standards across development environments.

Manage, monitor, and secure an Azure AI Foundry Service

• Monitor an Azure AI resource
• Manage costs for Azure AI Foundry Services
• Manage and protect account keys
• Manage authentication for an Azure AI Foundry Service resource

Summary: This section highlights the importance of efficiently running and protecting your Azure AI Foundry resources. You will learn how to monitor AI services for reliability, manage usage costs, and secure critical assets such as account keys and authentication methods.

The key idea is operational excellence, ensuring that once AI resources are deployed, they remain resilient, affordable, and well-guarded. Monitoring and protecting these systems ensures continuous compliance with both business and security goals.

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

Summary: This section emphasizes carrying out AI projects within ethical boundaries and ensuring solutions remain trustworthy. You will learn concrete techniques to moderate content, configure filters, and establish frameworks that steer AI behavior ethically.

The focus is not only on immediate safety measures but also long-term governance frameworks that build confidence in AI applications. By applying these principles, you ensure AI adoption is scalable, positive, and safeguarded against harmful misuse.

Domain 2: Implement generative AI solutions (18.33% of the exam)

Build generative AI solutions with Azure AI Foundry

• Plan and prepare for a generative AI solution
• Deploy a hub, project, and necessary resources with Azure AI 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 Azure AI Foundry SDK
• Utilize prompt templates in your generative AI solution

Summary: This section walks through everything you need to build complete generative AI systems using Azure AI Foundry. You will learn to provision foundational resources, deploy models, use prompt flows, and apply retrieval augmented generation (RAG) to ground your models in trusted data.

The goal is to move beyond experimentation into well-built generative solutions that can be integrated directly into custom applications. You'll also gain insights into prompt templates and how to evaluate flows for quality and efficiency.

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
• Implement an Azure OpenAI Assistant

Summary: This section focuses specifically on using Azure OpenAI to build real-world applications. You will explore how text, code, image, and multimodal models can be provisioned and deployed to suit your project requirements.

You will also discover how to integrate these powerful foundation models into your applications, leveraging assistants and multimodal experiences to create transformative user experiences. Practicality and creativity intersect here to unlock innovation.

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 an generative model

Summary: This section emphasizes operational excellence for generative AI projects. You will learn how to monitor resource consumption, optimize deployment strategies, and apply prompt engineering techniques to strengthen results.

The focus is to move beyond simple deployment and build efficient, secure, and cost-effective solutions. Fine-tuning models, scaling resources, and orchestrating multi-model workflows all highlight the maturity of a professional-level AI implementation.

Domain 3: Implement an agentic solution (8.33% of the exam)

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 Azure AI Foundry Agent Service
• Implement complex agents with Semantic Kernel and Autogen
• Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities
• Test, optimize and deploy an agent

Summary: This section introduces you to agent-based solutions, where custom agents can interact autonomously within applications. You will learn to provision resources, build agents, and understand the role these intelligent components play in workflow automation.

The section also explores advanced implementations like multi-agent orchestration, integration with Semantic Kernel, and testing strategies. This enables robust creation, optimization, and deployment of agents that enhance productivity and automation.

Domain 4: Implement computer vision solutions (13.33% of the exam)

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 AI Vision
• Convert handwritten text using Azure AI Vision

Summary: This section gives you the ability to extract high-value insights from images using Azure AI Vision. You will learn to analyze visual features, detect objects, and generate useful tags, gaining familiarity with the tools for both typed and handwritten text extraction.

The goal is to empower you with skills that support document analysis, accessibility features, and intelligent image recognition. These capabilities extend the use of computer vision into business workflows and customer experiences.

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

Summary: This section focuses on building and training models tailored to your data. You will learn how to implement classification and object detection, label images appropriately, and evaluate the effectiveness of custom models.

The section also provides a full lifecycle view of custom models, from training and publishing to deploying them for use. By going through each step, you will gain mastery over creating responsive AI experiences specific to your domain.

Analyze videos

• Use Azure AI Video Indexer to extract insights from a video or live stream
• Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

Summary: This section highlights video content analysis, enabling insights to be extracted from both stored and live video. Using services like Video Indexer and Spatial Analysis, you can extract metadata and detect events within streams.

The ability to analyze human presence and movement extends these tools to areas such as retail, safety, and live analytics. This ensures video data becomes a valuable resource for real-time and historical insights.

Domain 5: Implement natural language processing solutions (18.33% of the exam)

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 AI Translator service

Summary: This section equips you with the skills to draw meaning and patterns from text. You will analyze key phrases, entities, sentiment, and detect languages, as well as deploy translation capabilities.

Through these powerful capabilities, applications can be made more inclusive, localized, and intelligent, making text data a strategic asset in globalized environments.

Process and translate speech

• Integrate generative AI speaking capabilities in an application
• Implement text-to-speech and speech-to-text using Azure AI Speech
• Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
• Implement custom speech solutions with Azure AI Speech
• Implement intent and keyword recognition with Azure AI Speech
• Translate speech-to-speech and speech-to-text by using the Azure AI Speech service

Summary: This section explains how to work with Azure AI Speech to convert between spoken and written communication. You will learn to implement natural voice capabilities with text-to-speech, speech-to-text, and translation between formats and languages.

The aim is to enhance real-time interactions with applications, whether through custom assistant solutions, accessible design, or rich multilingual support. This makes interactions seamless and engaging for end users.

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

Summary: This section allows you to design custom natural language applications by training and integrating models that understand intent and context. You will be guided through building conversation-aware solutions and maintaining models effectively.

The practical outcome is the ability to deploy advanced conversational experiences such as chatbots, question answering systems, and multi-language interfaces, giving businesses interactive and adaptive tools for user engagement.

Domain 6: Implement knowledge mining and information extraction solutions (18.33% of the exam)

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

Summary: This section equips you to work with Azure AI Search, helping you create structured indexes and implement full query capabilities. You will build skillsets and custom skills that make indexing intelligent and responsive.

The power of search becomes clear when adding semantic and vector solutions to improve accuracy and user experience. These techniques transform raw data sources into easily discoverable and useful knowledge.

Implement an Azure AI Document Intelligence 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

Summary: This section demonstrates how to harness Azure AI Document Intelligence to extract meaningful information from structured and unstructured documents. By using both prebuilt and custom models, you can extend document processing to fit organizational needs.

The major advantage is automating tasks that save time and resources, with composed models enabling different document types to be supported seamlessly across workflows.

Extract information with Azure AI Content Understanding

• 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 AI Content Understanding

Summary: This section ensures you can extract diverse forms of content using Azure AI Content Understanding. From OCR pipelines to summarization and classification, you will explore techniques to refine multi-modal inputs.

The results lead to efficient knowledge pipelines, making content searchable and insights actionable. These capabilities support intelligent automation across industries where document and media processing are essential.