Prepare with Microsoft AI-102 Korean exam practice material, pass for sure
Updated: Oct 09, 2026
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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam
The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Korean Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure AI Fundamentals (AI-900) |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Passing Score: | 700 (scaled score out of 1000) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100 minutes |
| Exam Format: | Hot area, Case studies, Multiple choice, Drag-and-drop, Multiple select, Performance-based scenarios |
| Available Languages: | French, Spanish, English, Chinese (Simplified), Portuguese (Brazil), German, Italian, Indonesian, Japanese, Korean, Arabic (Saudi Arabia) |
| Recommended Training: | Microsoft Learn Learning Paths for AI-102 AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-102 Korean Sample Questions |
| Exam Way: | Online proctored (OnVUE) or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: experience with Azure services, AI concepts, and proficiency in Python or C#; AI-900 certification is recommended but not required |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102 |
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 processing architecture for a solution
- Select the appropriate data processing technologies
- Select the appropriate AI models and services
- Identify components and technologies required to connect service endpoints
- Identify automation requirements
Map security requirements to tools, technologies, and processes
- Identify auditing requirements
- 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
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 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 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 bot services that use Language Understanding (LUIS)
- Integrate bots with Azure app services and Azure Application Insights
- Design bots that integrate with channels
- Integrate bots and AI solutions
Design the compute infrastructure to support a solution
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
- Select a compute solution that meets cost constraints
- Identify whether to create a GPU, FPGA, or CPU-based solution
Design for data governance, compliance, integrity, and security
- Design a content moderation strategy for data usage within an AI solution
- 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
- Ensure appropriate governance of data
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Develop streaming solutions
- Develop AI pipelines
- Create solution endpoints
- Implement data logging processes
- Manage the flow of data through the solution components
- Define and construct interfaces for custom AI services
Integrate AI services with solution components
- Configure prerequisite components to allow connectivity to the Bot Framework
- 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
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
- Identify the differences between expected and actual workflow throughput
- Maintain an AI solution for continuous improvement
AI-102: Designing and Implementing an Azure AI Solution Certification Path
The Microsoft Designing and Implementing an Azure AI Solution Certification includes only one AI-100 Exam.
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services |
| Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers |
| Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Integrate vision capabilities into applications - Build and deploy custom vision models - Process and index video content - Extract text and handwriting from images |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
| Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots |
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