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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.
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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
How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement Decision Support Solutions | 10-15% | - Integrate Azure AI services into applications - Monitor and optimize decision support solutions - Design and implement decision automation |
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Create custom skills for Azure AI Search - Implement semantic and vector search - Implement Azure AI Search solutions - Design document processing pipelines |
| Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Implement speech capabilities using Azure AI Speech - Build question answering solutions - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech |
| Implement Computer Vision Solutions | 15-20% | - Implement object detection and image classification - Implement Azure AI Face service - Process video and generate insights - Analyze images using Azure AI Vision - Read and process documents using Azure AI Document Intelligence |
| Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Plan and configure secure AI solutions |
| Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Integrate Azure OpenAI models into applications - Implement prompt engineering techniques - Apply responsible AI practices |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) Sample Questions:
다음 파일이 있습니다.
* 파일1.pdf
* 파일2.jpg
* 파일3.docx
* 파일4.webp
* 파일S.png
Azure Al Content Understanding을 사용하여 어떤 파일을 분석할 수 있나요?
- A. File1.pdf, File2.jpg 및 File3.docx만
- B. File1.pdf, File2.jpg, File3.docx, File4.webp, File5.png
- C. File1 .pdf 및 File3.docx만
- D. File1.pdf, File2jpg, File5.png만
- E. File1.pdf, File2.jpg, File3.docx, FileS.png만
Correct Answer: E 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
참고: 이 문제는 동일한 시나리오를 제시하는 일련의 문제 중 하나입니다. 각 문제는 명시된 목표를 충족할 수 있는 고유한 답안을 포함하고 있습니다. 일부 문제 세트에는 정답이 두 개 이상 있을 수 있고, 다른 문제 세트에는 정답이 없을 수 있습니다.
이 섹션의 질문에 답변한 후에는 해당 질문으로 돌아갈 수 없습니다. 따라서 해당 질문은 복습 화면에 표시되지 않습니다.
Azure Cognitive Service에서 질문 답변 기능을 사용하는 챗봇이 있는데, 언어 사용자는 챗봇이 허위 질문에 답변할 때 형식적인 답변을 한다고 보고합니다. 챗봇이 허위 질문에 공식적인 답변을 제공하는지 확인해야 합니다.
해결책: Language Studio에서 모든 잡담 질문과 답변 쌍을 제거한 다음 모델을 다시 학습시키고 다시 게시합니다. 이렇게 하면 목표를 달성할 수 있을까요?
- A. 아니요
- B. 네
Correct Answer: A 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
당신은 스마트 전자상거래 프로젝트를 개발하고 있습니다.
인지 검색 솔루션의 일부로 자동 완성 기능을 구현해야 합니다.
어떤 세 가지 행동을 해야 할까요? 각 정답은 해결책의 일부를 나타냅니다. (세 가지를 선택하세요.) 참고: 각 정답은 1점입니다.
- A. 세 가지 제품명 필드 각각에 대한 추천 기능을 추가합니다.
- B. 검색 엔드포인트에 API 쿼리를 수행하고 searchFields 쿼리 매개변수에 제품 이름 필드를 포함합니다.
- C. 세 가지 제품명 변형에 대한 분석기 속성을 설정합니다.
- D. 세 가지 제품 이름 필드를 소스 필드로 사용하는 제안기를 추가합니다.
- E. 세 가지 제품명 변형에 대해 searchAnalyzer 속성을 설정합니다.
- F. 자동 완성 엔드포인트에 API 쿼리를 수행하고 본문에 suggesterName을 포함하세요.
Correct Answer: C,D,F 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
Face API 호출을 개발 중입니다. 이 호출은 employeefaces라는 기존 목록에서 유사한 얼굴을 찾아야 합니다. employeefaces 목록에는 60,000개의 이미지가 포함되어 있습니다.
HTTP 요청 본문을 어떻게 작성해야 할까요? 정답은 적절한 값을 해당 대상에 드래그하여 배치하는 것입니다. 각 값은 한 번, 여러 번 또는 전혀 사용되지 않을 수 있습니다. 콘텐츠를 보려면 창 사이의 분할 막대를 드래그하거나 스크롤해야 할 수도 있습니다.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
You are calling the Face API to find similar faces from an existing list called employeefaces.
Key facts from the scenario:
The list contains 60,000 images.
In Face API:
faceListId # limited to 1,000 faces.
largeFaceListId # designed for very large lists, up to 1,000,000 faces.
Since employeefaces has 60,000 images, you must use LargeFaceListId, not faceListId.
Parameters in the Request Body
" faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 "
This is the face to search for.
" largeFaceListId " : " employeefaces "
Must use LargeFaceListId because the list has 60,000 images.
" maxNumOfCandidatesReturned " : 1
Limits the number of matches returned.
" mode " : " matchFace "
Two options exist:
" matchFace " # returns the most similar individual faces.
" matchPerson " # groups faces by person.
Since the requirement is find similar faces, use " matchFace " .
Completed Answer
{
" faceId " : " 18c51a87-3a69-47a8-aedc-a54745f708a1 " ,
" largeFaceListId " : " employeefaces " ,
" maxNumOfCandidatesReturned " : 1 ,
" mode " : " matchFace "
}
Correct Values to Drag and Drop
LargeFaceListId # " employeefaces "
matchFace # " mode "
Final Answer:
First blank: LargeFaceListId
Second blank: matchFace
Microsoft References
Face API - Find Similar
Face API - Large Face List operations
다국어 챗봇을 만들고 있습니다.
긍정적 메시지와 부정적 메시지에 대해 각각 다른 답변을 보내야 합니다.
어떤 두 개의 Text Analytics API를 사용해야 합니까? 각 정답은 솔루션의 일부를 나타냅니다. (두 개 선택) 참고: 각 정답은 1점입니다.
- A. 명명된 엔터티 인식
- B. 감정 분석
- C. 핵심 문구
- D. 언어 감지
- E. 잘 알려진 지식 기반의 연결된 엔터티
Correct Answer: B,D 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
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