Microsoft AI-102 Korean Q&A - in .pdf

  • AI-102 Korean pdf
  • Exam Code: AI-102 Korean
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
  • Updated: Sep 12, 2026
  • Q & A: 425 Questions and Answers
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Microsoft AI-102 Korean Q&A - Testing Engine

  • AI-102 Korean Testing Engine
  • Exam Code: AI-102 Korean
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
  • Updated: Sep 12, 2026
  • Q & A: 425 Questions and Answers
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Topics of AI-102: Designing and Implementing an Azure AI Solution Exam

Candidates should apprehend the examination topics before they begin of preparation. because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:

1. Analyze solution requirements (25-30%)

Recommend Cognitive Services APIs to meet business requirements

  • Select the appropriate data processing technologies
  • Identify components and technologies required to connect service endpoints
  • Select the appropriate AI models and services
  • Identify automation requirements
  • Select the processing architecture for a solution

Map security requirements to tools, technologies, and processes

  • Identify appropriate tools for a solution
  • Identify auditing requirements
  • Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
  • Identify which users and groups have access to information and interfaces

Select the software, services, and storage required to support a solution

  • Identify appropriate services and tools for a solution
  • Identify integration points with other Microsoft services
  • Identify storage required to store logging, bot state data, and Cognitive Services output

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

  • Define an AI application workflow process
  • Select an AI solution that meet cost constraints
  • Design pipelines that use AI apps
  • Design pipelines that call Azure Machine Learning models
  • Design a strategy for ingest and egress data
  • Design the integration point between multiple workflows and pipelines

Design solutions that uses Cognitive Services

  • Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

Design solutions that implement the Bot Framework

  • Design bot services that use Language Understanding (LUIS)
  • Integrate bots with Azure app services and Azure Application Insights
  • Integrate bots and AI solutions
  • Design bots that integrate with channels

Design the compute infrastructure to support a solution

  • Select a compute solution that meets cost constraints
  • Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
  • Identify whether to create a GPU, FPGA, or CPU-based solution

Design for data governance, compliance, integrity, and security

  • Design strategies to ensure that the solution meets data privacy regulations and industry standards
  • Define how users and applications will authenticate to AI services
  • Ensure that data adheres to compliance requirements defined by your organization
  • Ensure appropriate governance of data
  • Design a content moderation strategy for data usage within an AI solution

3. Implement and monitor AI solutions (25-30%)

Implement an AI workflow

  • Create solution endpoints
  • Define and construct interfaces for custom AI services
  • Manage the flow of data through the solution components
  • Implement data logging processes
  • Develop streaming solutions
  • Develop AI pipelines

Integrate AI services with solution components

  • Configure prerequisite components to allow connectivity to the Bot Framework
  • Implement Azure Search in a solution
  • Configure integration with Cognitive Services
  • Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs

Monitor and evaluate the AI environment

  • Identify the differences between expected and actual workflow throughput
  • Identify the differences between KPIs, reported metrics, and root causes of the differences
  • Monitor AI components for availability
  • Recommend changes to an AI solution based on performance data
  • Maintain an AI solution for continuous improvement

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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam

  • Type of Questions: This test format is multiple choice.
  • This is beta exam.
  • Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
  • Length of Examination: 50 mins
  • Passing Score: 700 / 1000
  • Language: English, Japanese, Chinese (Simplified), Korean.

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

Microsoft AI-102 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement generative AI solutions15-20%- Orchestrate multiple models and containers
- Integrate Azure OpenAI and other generative models
- Deploy and manage generative models
- Implement model monitoring and feedback
- Apply prompt engineering and fine-tuning
Topic 2: Implement natural language processing solutions15-20%- Customize and deploy NLP models
- Build conversational AI and chatbots
- Perform text analysis, sentiment detection, and language detection
- Implement translation and summarization
Topic 3: Implement computer vision solutions10-15%- Analyze images and detect objects/features
- Integrate vision capabilities into applications
- Extract text and handwriting from images
- Process and index video content
- Build and deploy custom vision models
Topic 4: Implement knowledge mining and information extraction solutions15-20%- Ingest and process structured/unstructured data
- Implement intelligent search and retrieval
- Build knowledge bases and search indexes
- Extract entities, relationships, and key phrases
Topic 5: 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
Topic 6: Plan and manage an Azure AI solution20-25%- Plan solutions aligned with responsible AI principles
- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
- Select suitable AI models
- Create and configure Azure AI resources
- Select appropriate Microsoft Foundry Services
- Monitor, optimize, and secure AI solutions
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