Microsoft AI-102 Exam : Designing and Implementing a Microsoft Azure AI Solution

  • Exam Code: AI-102
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution
  • Updated: Jun 22, 2026
  • Q & A: 416 Questions and Answers

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About Microsoft AI-102 Exam Questions

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  • This certification will be judging your skills and knowledge on your understanding Designing and Implementing an Azure AI Solution concepts & Understanding of how to operate on Planning and Administering Designing and Implementing an Azure AI Solution .

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

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

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

Design the compute infrastructure to support a solution

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

Design for data governance, compliance, integrity, and security

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

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

Implement an AI workflow

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

Integrate AI services with solution components

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

Monitor and evaluate the AI environment

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

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

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