Microsoft AI-102日本語 dumps - in .pdf

AI-102日本語 pdf
  • Exam Code: AI-102日本語
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Aug 09, 2026
  • Q & A: 425 Questions and Answers
  • PDF Price: $69.99
  • Free Demo

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  • Exam Code: AI-102日本語
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Aug 09, 2026
  • Q & A: 425 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $139.98  $89.99
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Microsoft AI-102日本語 dumps - Testing Engine

AI-102日本語 Testing Engine
  • Exam Code: AI-102日本語
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Aug 09, 2026
  • Q & A: 425 Questions and Answers
  • Software Price: $69.99
  • Testing Engine

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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

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

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

Map security requirements to tools, technologies, and processes

  • Identify appropriate tools for a solution
  • 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 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 integration points with other Microsoft services
  • Identify appropriate services and tools for a solution

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

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

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 and AI solutions
  • Integrate bots with Azure app services and Azure Application Insights

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

  • Define how users and applications will authenticate to AI services
  • Ensure appropriate governance of data
  • Ensure that data adheres to compliance requirements defined by your organization
  • 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

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

Implement an AI workflow

  • Implement data logging processes
  • Develop AI pipelines
  • Create solution endpoints
  • Develop streaming solutions
  • 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 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

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

Microsoft AI-102日本語 Exam Syllabus Topics:

SectionWeightObjectives
Implement knowledge mining and information extraction solutions15-20%- Build knowledge bases and search indexes
- Ingest and process structured/unstructured data
- Implement intelligent search and retrieval
- Extract entities, relationships, and key phrases
Implement natural language processing solutions15-20%- Implement translation and summarization
- Customize and deploy NLP models
- Perform text analysis, sentiment detection, and language detection
- Build conversational AI and chatbots
Implement generative AI solutions15-20%- Implement model monitoring and feedback
- Deploy and manage generative models
- Orchestrate multiple models and containers
- Integrate Azure OpenAI and other generative models
- Apply prompt engineering and fine-tuning
Implement an agentic solution5-10%- Test, deploy, and optimize agents
- Understand agent use cases and types
- Develop multi-agent workflows and orchestration
- Build agents with Microsoft Foundry Agent Service
Plan and manage an Azure AI solution20-25%- Select suitable AI models
- Monitor, optimize, and secure AI solutions
- Plan solutions aligned with responsible AI principles
- Create and configure Azure AI resources
- Select appropriate Microsoft Foundry Services
- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
Implement computer vision solutions10-15%- Integrate vision capabilities into applications
- Build and deploy custom vision models
- Process and index video content
- Analyze images and detect objects/features
- Extract text and handwriting from images

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