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Microsoft Designing and Implementing a Microsoft Azure AI Solution - AI-102 real prep

AI-102
  • Exam Code: AI-102
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution
  • Updated: Aug 06, 2026
  • Q & A: 425 Questions and Answers
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About Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution

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

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

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

  • Design a strategy for ingest and egress data
  • Select an AI solution that meet cost constraints
  • Design pipelines that call Azure Machine Learning models
  • Design pipelines that use AI apps
  • Define an AI application workflow process
  • 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
  • Integrate bots with Azure app services and Azure Application Insights
  • Design bot services that use Language Understanding (LUIS)
  • Integrate bots and AI solutions

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
  • 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 appropriate governance of data

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

Implement an AI workflow

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

Integrate AI services with solution components

  • Configure integration with Cognitive Services
  • 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

Monitor and evaluate the AI environment

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

AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary

The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in

  • England - 90,532 POUND
  • United State - 120,000 USD
  • Europe - 90,547 EURO
  • India - 20,00,327 INR

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.

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

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Microsoft AI-102 Exam Syllabus Topics:

SectionWeightObjectives
Implement Knowledge Mining and Document Intelligence Solutions15-20%- Create custom skills for Azure AI Search
- Implement Azure AI Search solutions
- Implement semantic and vector search
- Design document processing pipelines
Implement Generative AI Solutions10-15%- Use Azure OpenAI Service for generative AI
- Apply responsible AI practices
- Implement prompt engineering techniques
- Integrate Azure OpenAI models into applications
Implement Computer Vision Solutions15-20%- Process video and generate insights
- Read and process documents using Azure AI Document Intelligence
- Analyze images using Azure AI Vision
- Implement object detection and image classification
- Implement Azure AI Face service
Implement Decision Support Solutions10-15%- Design and implement decision automation
- Monitor and optimize decision support solutions
- Integrate Azure AI services into applications
Plan and Manage an Azure AI Solution15-20%- Plan and configure secure AI solutions
- Select appropriate Azure AI services and technologies
- Manage AI costs and resource allocation
- Implement governance and compliance for AI solutions
Implement Natural Language Processing Solutions20-25%- Build question answering solutions
- Implement speech capabilities using Azure AI Speech
- Analyze text using Azure AI Language
- Translate text and speech
- Create conversational AI solutions with Azure AI Bot Service

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