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Microsoft AI-102-KR guide torrent - Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)

Updated: Aug 04, 2026

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AI-102 Korean guide torrent
  • Exam Code: AI-102-KR
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)

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Microsoft AI-102 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement computer vision solutions: This domain addresses image and video analysis using Azure Vision services, including object detection, text extraction, training custom vision models, and extracting insights from video streams.
Topic 2
  • Implement natural language processing solutions: This domain involves text and speech processing, including sentiment analysis, translation, speech-to-text and text-to-speech implementation, building language understanding models, and creating question answering systems.
Topic 3
  • Implement an agentic solution: This domain covers creating autonomous AI agents using Microsoft Foundry Agent Service and Framework, implementing multi-agent workflows, and testing and deploying intelligent agents.
Topic 4
  • Implement generative AI solutions: This domain focuses on building solutions with Microsoft Foundry and Azure OpenAI, including deploying models, implementing prompt flows and RAG patterns, generating content, and optimizing through parameter tuning and prompt engineering.

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

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Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:

Schedule ExamPearson VUE
Exam NameMicrosoft Certified - Azure AI Engineer Associate
Duration130 mins
Exam Price$165 (USD)
Passing Score700 / 1000
Sample QuestionsDesigning and Implementing a Microsoft Azure AI Solution Sample Questions
Number of Questions40-60
Exam CodeAI-102
Books / TrainingCourse AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

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

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

Map security requirements to tools, technologies, and processes

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

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

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

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

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

Design the compute infrastructure to support a solution

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

Design for data governance, compliance, integrity, and security

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

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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