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Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean real prep

AI-102 Korean
  • Exam Code: AI-102-KR
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
  • Updated: Sep 05, 2025
  • Q & A: 381 Questions and Answers
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Microsoft AI-102 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Plan and manage an Azure AI solution: This exam topic covers selecting the appropriate Azure AI service; it covers how to plan, create, and deploy an Azure AI service and how to manage, monitor, and secure an Azure AI service.
Topic 2
  • Implement knowledge mining and document intelligence solutions: This topic of the exam discusses implementing an Azure AI Search solution and implementing an Azure AI Document Intelligence solution.
Topic 3
  • Implement content moderation solutions: This section of the exam covers creating solutions for content delivery that involve deploying a text moderation solution with Azure AI Content Safety.
Topic 4
  • Implement natural language processing solutions: This section of the exam covers examining text by using Azure AI-Language, processing speech by using Azure AI Speech, translating language, and deploying a language understanding model by using Azure AI-Language.

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

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.

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

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

Map security requirements to tools, technologies, and processes

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

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

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

  • 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
  • Select an AI solution that meet cost constraints
  • 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

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

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

  • 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 strategies to ensure that the solution meets data privacy regulations and industry standards
  • Design a content moderation strategy for data usage within an AI solution

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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