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Last Updated: Oct 10, 2026
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Microsoft AI-102 Korean Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure AI Fundamentals (AI-900) Microsoft Certified: Azure Developer Associate |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (scaled score out of 1000) |
| Exam Format: | Case studies, Performance-based scenarios, Multiple select, Hot area, Drag-and-drop, Multiple choice |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Available Languages: | French, Indonesian, Italian, Arabic (Saudi Arabia), Portuguese (Brazil), Japanese, English, Chinese (Simplified), Spanish, Korean, German |
| Recommended Training: | AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Microsoft Learn Learning Paths for AI-102 |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | DOWNLOAD DEMO |
| 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 |
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Build and deploy custom vision models - Process and index video content - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 2: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Understand agent use cases and types |
| Topic 3: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
| Topic 4: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization |
| Topic 5: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Select suitable AI models |
| Topic 6: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes |
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The latest exam information lists 40-60 questions for the AI-102 Korean exam within a 100 minutes-minute window. Practicing under those exact conditions is the best cure for exam-day surprises.
The Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) blueprint is organized around these core domains:
- Implement an agentic solution (5-10%)
- Implement knowledge mining and information extraction solutions (15-20%)
- Implement generative AI solutions (15-20%)
Further domains appear in the full official outline; the question bank covers all of them.
By fitting study into the cracks of a busy life. The AI-102 Korean bank contains complete questions with expert-verified answers across the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) objectives, stored easily on your phone, pad, or computer for spare-time review — and the PDF prints cleanly when you prefer paper, because marking key points by hand genuinely enhances memory. Assess everything first through the free demo on our site, pay securely through the Credit Card system, and rest assured your personal information is never shared with third parties without permission.
Microsoft lists the following official training resources:
- AI-102T00: Designing and Implementing a Microsoft Azure AI Solution
- Microsoft Learn Learning Paths for AI-102
Structured courses pair well with spare-time question practice for steady, low-stress progress.
Microsoft specifies these prerequisites for the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version): No formal prerequisites; recommended: experience with Azure services, AI concepts, and proficiency in Python or C#; AI-900 certification is recommended but not required.
Check the authoritative wording on the official certification page before booking.
At present, the AI-102 Korean exam requires a passing score of 700 (scaled score out of 1000), and registration costs $165 USD (varies by region: £113 GBP, €126 EUR). Microsoft sets both figures and may adjust them, so verify on the official site before you schedule.
Use the official registration channels below:
Pick a test center or online slot that suits your routine, and book early — calm planning beats last-minute pressure.
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) Sample Questions:
Microsoft Bot Framework SDK를 사용하여 봇을 만들 수 있습니다.
사용자 정의 텍스트 응답을 사용하여 이벤트에 응답하도록 봇을 구성해야 합니다.
무엇을 사용해야 하나요?
- A. 대화
- B. 기술
- C. 활동 핸들러
- D. 적응형 카드
Correct Answer: C 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
Azure AI Speech 서비스와 사용자 지정 신경망 음성을 사용하는 전화 통화 처리 솔루션을 구축하고 있습니다.
사용자 정의 음성 모델을 만들어야 합니다.
Speech Studio에서 어떤 다섯 가지 동작을 순서대로 수행해야 할까요? 답변하려면 동작 목록에서 해당 동작을 답변 영역으로 옮기고 올바른 순서대로 정렬하세요.
Correct Answer:
Explanation:
Comprehensive Detailed Explanation
A custom neural voice (professional voice) project in Azure AI Speech requires that you follow Responsible AI and consent steps before any model training. The correct workflow in Speech Studio / Azure AI Foundry is:
Create a custom voice project - This is the container that will hold the voice talent consent, your training datasets, models, and deployments. Each project is scoped to a locale and other attributes. Microsoft Learn Collect and upload consent - Microsoft requires explicit authorization from the voice talent. In practice, you add the voice talent consent to the project. The platform requires the verbal consent recording (WAV); many guidance and exam items also include the signed consent document (PDF) as part of consent capture to meet Responsible AI expectations. The consent step is performed before uploading training data.
Upload the voice talent's verbal consent as a WAV file - The consent audio verifies that the person who granted consent is the same as the speaker in your training data. The documentation shows consent is a recorded statement uploaded to the project. Microsoft Learn Analyze the quality of the audio data and fix issues - After data upload, Speech Studio runs validation and provides a per-utterance analysis (format checks, sampling rate, script match, pronunciation/noise scoring).
You must resolve issues and run Analyze data before the dataset can be used for training. Microsoft Learn Train the model using a neural training method - Start model training from the project and choose Neural as the training type for custom neural voice. Microsoft Learn Why not "upload speech samples as WMA/MP3 files"?
For the standard, recommended Individual utterances + matching transcript data type used to fine-tune a professional neural voice, the audio files must be WAV/RIFF PCM; the docs explicitly reference ".wav" throughout data checks and corrections. WMA isn't supported, and MP3 isn't accepted for this core data type.
(Long audio / audio-only workflows can accept MP3 for preprocessing, but the canonical training dataset for custom neural voice is WAV.) Microsoft Azure AI References Create a project for professional voice (Speech Studio/Azure AI Foundry): Project contains consent, datasets, models, endpoints. Microsoft Learn Add voice talent consent to the professional voice project: Upload the recorded consent statement for the voice talent. Microsoft Learn Professional voice fine-tuning data - required formats: Individual utterances + transcript require WAV/RIFF PCM; guidance on sample rate and transcript format. Microsoft Learn Add a professional voice training dataset & resolve data issues: Data validation, pronunciation/noise checks, and the Analyze data step prior to training. Microsoft Le arn Train your professional voice model: Start training and select Neural method. Microsoft Learn
Azure OpenAI 리소스가 포함된 Azure 구독이 있습니다.
GPT-4 모델을 리소스에 배포합니다.
모델의 기초 데이터로 사용될 파일을 업로드할 수 있는지 확인해야 합니다.
어떤 두 가지 유형의 리소스를 만들어야 할까요? 각 정답은 해결책의 일부를 제시합니다.
참고: 정답 하나당 1점입니다.
- A. Azure SQL
- B. Azure Blob 저장소
- C. Azure AI Bot 서비스
- D. Azure Al 검색
- E. Azure AI 문서 인텔리전스
Correct Answer: B,D 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
Microsoft Bot Framework를 사용하여 빌드된 챗봇이 있습니다. 챗봇 엔드포인트를 원격으로 디버깅해야 합니다.
로컬 컴퓨터에 어떤 두 도구를 설치해야 합니까? 각 정답은 솔루션의 일부를 나타냅니다. (두 개를 선택하세요.) 참고: 각 정답은 1점입니다.
- A. 봇 프레임워크 에뮬레이터
- B. nginx
- C. 응그록
- D. 바이올리니스트
- E. 봇 프레임워크 CLI
- F. Bot Framework Composer
Correct Answer: A,C 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
사용자가 업로드한 여러 파일을 결합하고 처리하는 에이전트를 구축할 계획입니다. 이 에이전트 개발에 Microsoft Foundry Agent Service를 사용할지 여부를 검토 중입니다. 해당 서비스에 업로드할 수 있는 각 파일의 최대 크기는 얼마입니까?
- A. 512MB
【용량】: 1GB - B. 10MB
- C. 256MB
Correct Answer: A 🗳️
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