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Exam Overview
This certification test is available in English, Spanish, Simplified Chinese, Japanese, French, Korean, and German. You will find different formats of questions while dealing with this Microsoft exam. These include multiple choice, drag and drop, build list, active screen, short answer, and best answer. The test costs $99 and the learners can register for it through Pearson VUE or Certiport.
The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft Azure AI Fundamentals Exam Certification Details:
| Passing Score | 700 / 1000 |
| Schedule Exam | Pearson VUE |
| Duration | 60 mins |
| Exam Code | AI-900 |
| Exam Price | $99 (USD) |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Number of Questions | 40-60 |
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:
Question #1
어떤 상황에서 핵심어 추출을 사용해야 할까요?
A. 레스토랑에 대한 리뷰가 긍정적인지 부정적인지 식별
B. 오디오 트랙을 기반으로 비디오에 대한 자막 생성
C. 동일한 주제에 대한 정보를 제공하는 문서 식별
D. 영어에서 독일어로 문서 세트 번역
Question #2
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
Question #3
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
Question #4
Azure Al Vision 서비스를 사용하여 어떤 작업을 수행할 수 있나요?
A. 손으로 쓴 편지에서 데이터 추출
B. 라이브 비디오 스트림에서 동물 품종 식별
C. 문서에서 핵심 구문 추출
D. 교육용 비디오 썸네일 만들기
Question #5
기존 데이터 세트에서 훈련 데이터 세트와 검증 데이터 세트를 만들어야 합니다.
Azure Machine Learning Designer에서 어떤 모듈을 사용해야 하나요?
A. 데이터 분할
B. 데이터 조인
C. 데이터 세트에서 열 선택
D. 행 추가
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: Only visible for members | Question #3 Correct Answer: Only visible for members | Question #4 Correct Answer: B | Question #5 Correct Answer: A |
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