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Microsoft AI-102日本語 real answers - Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)

AI-102日本語
  • Exam Code: AI-102J
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Jul 10, 2025
  • Q & A: 372 Questions and Answers
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How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=AI-102&examname=Exam%20AI-102:%20Designing%20and%20Implementing%20a%20Microsoft%20Azure%20AI%20Solution&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fai-102

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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

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

Map security requirements to tools, technologies, and processes

  • 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
  • Identify appropriate tools for a solution

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

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

  • Design pipelines that call Azure Machine Learning models
  • Select an AI solution that meet cost constraints
  • Define an AI application workflow process
  • Design pipelines that use AI apps
  • 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 bots that integrate with channels
  • Design bot services that use Language Understanding (LUIS)
  • Integrate bots with Azure app services and Azure Application Insights
  • 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 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
  • Design strategies to ensure that the solution meets data privacy regulations and industry standards
  • Define how users and applications will authenticate to AI services

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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

Only need to practice for 20 to 30 hours

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