Microsoft AI-102-KR exam dumps : Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)

  • Exam Code: AI-102-KR
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
  • Updated: Jul 27, 2026     Q & A: 425 Questions and Answers

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

Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate

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

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For more info read reference:

microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services

How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam

Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam

Introduction

Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.

This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.

For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.

Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.

In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.

Microsoft AI-102 Korean Exam Syllabus Topics:

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

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