AI-102: Microsoft Azure AI Solution Practice Exams Prep 2026

AI-102 Microsoft Azure AI Solution Certification Practice Exam is a comprehensive and reliable tool designed to help individuals prepare for the Microsoft Azure AI Solution Certification exam. This practice exam offers a range of benefits to users, including the opportunity to assess their knowledge and skills in the field of AI, gain confidence in their abilities, and identify areas for improvement.

With a focus on practical application and real-world scenarios, the AI-102 practice exam provides users with a realistic and challenging experience that closely mirrors the actual certification exam. This allows individuals to become familiar with the exam format, question types, and time constraints, enabling them to perform at their best on exam day.

In addition to its practical benefits, the AI-102 practice exam is also an excellent resource for individuals seeking to enhance their professional credentials and advance their careers in the field of AI. By earning the Microsoft Azure AI Solution Certification, individuals can demonstrate their expertise and proficiency in designing and implementing AI solutions using Microsoft Azure technologies, opening up new opportunities for career growth and advancement.

Microsoft Azure AI Solution Exam Summary:

• Exam Name : Microsoft Azure AI Solution

• Exam Code : AI-102

• Exam Price : 165 (USD)

• Number of Questions: Maximum of 40-60 questions,

• Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

• Length of Test: 130 Minutes. The exam is available in English and Japanese languages.

• Passing Score: 700 / 1000

• Languages : English at launch. Japanese

• Schedule Exam : Pearson VUE

Microsoft AI-102 Exam Syllabus Topics:

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 Azure AI 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 an Azure AI 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 services into a continuous integration and continuous delivery (CI/CD) pipeline

• Plan and implement a container deployment

Manage, monitor, and secure an Azure AI service

• Monitor an Azure AI resource

• Manage costs for Azure AI services

• Manage and protect account keys

• Manage authentication for an Azure AI 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 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

Use Azure OpenAI Service to generate content

• Provision an Azure OpenAI Service 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

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

  
  

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 Azure AI 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

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 AI Vision

• Convert handwritten text using Azure AI Vision

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 AI Vision 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 AI Translator service

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

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 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

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

Overall, the AI-102 Microsoft Azure AI Solution Certification Practice Exam is an essential tool for anyone seeking to achieve certification in this rapidly growing field. With its comprehensive coverage, practical focus, and numerous benefits, this practice exam is an invaluable resource for individuals looking to take their AI skills and knowledge to the next level.