Microsoft Designing and Implementing a Microsoft Azure AI Solution : AI-102

AI-102 real exams

Exam Code: AI-102

Exam Name: Designing and Implementing a Microsoft Azure AI Solution

Updated: Aug 01, 2026

Q & A: 425 Questions and Answers

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Prerequisites

Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.

Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.

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Microsoft AI-102 Exam Syllabus Topics:

SectionWeightObjectives
Implement an agentic solution5-10%- Understand agent use cases and types
- Build agents with Microsoft Foundry Agent Service
- Test, deploy, and optimize agents
- Develop multi-agent workflows and orchestration
Plan and manage an Azure AI solution20-25%- Monitor, optimize, and secure AI solutions
- Plan solutions aligned with responsible AI principles
- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
- Create and configure Azure AI resources
- Select appropriate Microsoft Foundry Services
- Select suitable AI models
Implement generative AI solutions15-20%- Deploy and manage generative models
- Integrate Azure OpenAI and other generative models
- Implement model monitoring and feedback
- Apply prompt engineering and fine-tuning
- Orchestrate multiple models and containers
Implement knowledge mining and information extraction solutions15-20%- Ingest and process structured/unstructured data
- Extract entities, relationships, and key phrases
- Build knowledge bases and search indexes
- Implement intelligent search and retrieval
Implement natural language processing solutions15-20%- Build conversational AI and chatbots
- Perform text analysis, sentiment detection, and language detection
- Customize and deploy NLP models
- Implement translation and summarization
Implement computer vision solutions10-15%- Integrate vision capabilities into applications
- Extract text and handwriting from images
- Process and index video content
- Build and deploy custom vision models
- Analyze images and detect objects/features

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