Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version): AI-102 Exam
"Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)", also known as AI-102 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 425 Q&As to your AI-102 Exam preparation. In the AI-102 exam resources, you will cover every field and category in Azure AI Engineer Associate Certification helping to ready you for your successful Microsoft Certification.
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- Exam Code: AI-102
- Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
- Certification Provider: Microsoft
- Corresponding Certification: Azure AI Engineer Associate
- Updated: Oct 08, 2026
- No. of Questions: 425 Questions & Answers with Testing Engine
- Download Limit: Unlimited
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Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Duration | 130 mins |
| Exam Price | $165 (USD) |
| Schedule Exam | Pearson VUE |
| Passing Score | 700 / 1000 |
| Exam Code | AI-102 |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Number of Questions | 40-60 |
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
- Identify components and technologies required to connect service endpoints
- Select the appropriate data processing technologies
- Identify automation requirements
- Select the processing architecture for a solution
Map security requirements to tools, technologies, and processes
- Identify which users and groups have access to information and interfaces
- Identify appropriate tools for a solution
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify auditing 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
- Define an AI application workflow process
- Design the integration point between multiple workflows and pipelines
- Select an AI solution that meet cost constraints
- Design pipelines that call Azure Machine Learning models
- Design pipelines that use AI apps
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 and AI solutions
- Integrate bots with Azure app services and Azure Application Insights
- Design bot services that use Language Understanding (LUIS)
- Design bots that integrate with channels
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
- Ensure that data adheres to compliance requirements defined by your organization
- 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 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
- Define and construct interfaces for custom AI services
- Develop streaming solutions
- Create solution endpoints
- Implement data logging processes
Integrate AI services with solution components
- Implement Azure Search in a solution
- 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
Monitor and evaluate the AI environment
- Identify the differences between expected and actual workflow throughput
- Monitor AI components for availability
- Maintain an AI solution for continuous improvement
- Recommend changes to an AI solution based on performance data
- Identify the differences between KPIs, reported metrics, and root causes of the differences
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.
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | - Manage security, compliance, and responsible AI - Plan solution requirements and architecture - Select appropriate Azure AI services |
| Topic 2: Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Implement document intelligence solutions - Enrich and index data for search scenarios |
| Topic 3: Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision - Implement custom vision models |
| Topic 4: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
| Topic 5: Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language - Implement question answering and conversational AI |
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