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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Implement custom vision models - Analyze images and videos using Azure AI Vision |
| Topic 2: Implement Knowledge Mining and Document Intelligence | - Enrich and index data for search scenarios - Build Azure AI Search solutions - Implement document intelligence solutions |
| Topic 3: Implement Natural Language Processing Solutions | - Analyze text using Azure AI Language - Implement question answering and conversational AI - Translate and interpret language using Azure AI services |
| Topic 4: Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Manage security, compliance, and responsible AI - Plan solution requirements and architecture |
| Topic 5: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
AI-102 Korean Exam Details Every Candidate Should Check
You need 700 (out of 1000) to pass the AI-102 Korean exam, and registration costs USD 165 (varies by country/region). Since each attempt carries the fee, candidates who practice thoroughly first — on paper, on screen, under time pressure — put their money behind preparation rather than retakes.
The AI-102 Korean exam is the official test for the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) certification from Microsoft. For graduates and job seekers facing drastic competition, it does something a resume line cannot: it verifies your skills against a recognized standard, so when you enter an interview process, the credential helps you stand out on evidence rather than adjectives.
The official outline divides the AI-102 Korean exam into weighted domains, including:
- Plan and Manage an Azure AI Solution ()
- Implement Natural Language Processing Solutions ()
- Implement Generative AI Solutions ()
Print the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) PDF from PassExamDumps and mark these domains' key points directly on paper — annotating the difficult parts is a proven way to make them stick.
No formal prerequisites required, but familiarity with Azure fundamentals and AI/ML concepts is recommended
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The AI-102 Korean exam includes 40-60 (approximate range) questions to be completed within 100-120 minutes. Time is money during preparation too — focused practice on exam-aligned questions is the shortest route to feeling ready.
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Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) Sample Questions:
Azure Resource Manager(ARM) 템플릿을 사용하여 Azure OpenAI 리소스를 배포하려고 합니다.
리소스가 분당 600개의 요청에 응답할 수 있는지 확인해야 합니다.
템플릿은 어떻게 작성해야 하나요? 답변하려면 답변 영역에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object.
Specifically:
Use the capacity field of the SKU to specify the per-deployment rate.
Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.
Therefore, to meet the requirement of 600 requests per minute , complete the template as:
" sku " : {
" name " : " Standard " ,
" capacity " : 600
}
This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.
Microsoft Azure AI Solution References
ARM template-Microsoft.CognitiveServices/accounts/deployments (2023-05-01) : property reference showing sku.capacity. Microsoft Learn Resource type reference-latest accounts/deployments : confirms SKU usage on deployments. Microsoft Learn Azure OpenAI quota and rate limits : explains assigning deployment-level limits (RPM/TPM). Microsoft Learn
결정 및 언어 API를 사용하는 앱을 개발하고 있습니다.
앱에 대한 리소스를 프로비저닝해야 합니다. 솔루션은 각 서비스에 단일 엔드포인트와 자격 증명을 사용하여 액세스할 수 있도록 해야 합니다.
어떤 유형의 리소스를 만들어야 할까요?
- A. Azure Cognitive Services
- B. 콘텐츠 관리자
- C. 언어
- D. 연설
Correct Answer: A 🗳️
Azure 구독과 10,000개의 ASCII 파일이 있습니다.
특정 구문을 포함하는 파일을 식별해야 합니다. 솔루션은 코사인 유사도를 사용해야 합니다.
어떤 Azure OpenAI 모델을 사용해야 합니까?
- A. GPT-4
- B. GPT-4-32k
- C. 텍스트 임베딩-ada-002
- D. GPT-35 터보
Correct Answer: C 🗳️
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Form Recognizer를 사용하여 구매 주문을 인덱싱하는 Azure Cognitive Search 인스턴스가 있습니다. Microsoft Power BI를 사용하여 추출된 정보를 분석해야 합니다. 이 솔루션은 개발 노력을 최소화해야 합니다.
인덱서에 무엇을 추가해야 하나요?
- A. 테이블 투영
- B. 파일 프로젝션
- C. 투영 그룹
- D. 객체 투영
Correct Answer: A 🗳️
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CS1이라는 이름의 Azure Al Content Safety 리소스가 포함된 Azure 구독이 있습니다.
원이 포함된 테스트 이미지를 만듭니다.
curl 명령과 다음 명령줄 매개변수를 사용하여 테스트 이미지를 CS1에 제출합니다.
어떤 결과를 기대해야 하나요?
- A. 100
- B. 0.0
- C. 0
- D. 7
Correct Answer: B 🗳️
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