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Microsoft AI-102日本語 Valid Braindumps - 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 17, 2026
  • Q & A: 425 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

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

  • Select the appropriate AI models and services
  • Select the appropriate data processing technologies
  • Identify automation requirements
  • Identify components and technologies required to connect service endpoints
  • 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 which users and groups have access to information and interfaces
  • Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements

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

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

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

Design the compute infrastructure to support a solution

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

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

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

Integrate AI services with solution components

  • 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
  • Implement Azure Search in a solution

Monitor and evaluate the AI environment

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

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Prerequisites

Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.

How to book the AI-102: Designing and Implementing an Azure AI Solution Exam

These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.

  • Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
  • Step 2: Sign up/Login to Pearson VUE account
  • Step 3: Select local centre based on your country, date, time and confirm with a payment method.

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