An efficient method beats a heroic effort. CertkingdomPDF's AI-900 Korean question bank gives you both structure and substance: 336+ Q&As covering the Microsoft Azure AI Fundamentals (AI-900 Korean Version) objectives with expert-verified answers, ready the minute you buy.
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe generative AI concepts |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines |
| Topic 5: 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 Speech - Describe capabilities of Azure Language |
What to Know Before Your AI-900 Korean Exam Day
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The latest exam information lists 40–60 questions for the AI-900 Korean exam within a 45 minutes-minute window. Practicing under those exact conditions is the best cure for exam-day surprises.
The Microsoft Azure AI Fundamentals (AI-900 Korean Version) blueprint is organized around these core domains:
- Fundamental principles of machine learning on Azure (15–20%)
- Features of computer vision workloads on Azure (15–20%)
- Features of Natural Language Processing (NLP) workloads on Azure (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-900 Korean bank contains complete questions with expert-verified answers across the Microsoft Azure AI Fundamentals (AI-900 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:
Structured courses pair well with spare-time question practice for steady, low-stress progress.
Microsoft specifies these prerequisites for the Microsoft Azure AI Fundamentals (AI-900 Korean Version): No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory.
Check the authoritative wording on the official certification page before booking.
At present, the AI-900 Korean exam requires a passing score of 700 (on a scale of 1–1000), and registration costs $99 USD. Microsoft sets both figures and may adjust them, so verify on the official site before you schedule.
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Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of the Computer Vision and Custom Vision services", the Custom Vision service is used to train, deploy, and improve custom image classification and object detection models using your own labeled data.
* Multilabel or Multiclass Selection - NOThe statement is false because the Multilabel or Multiclass choice applies only to image classification models, not object detection models. In image classification,
"Multiclass" means one label per image, while "Multilabel" means multiple labels per image. In contrast, object detection models identify and locate multiple objects in an image using bounding boxes; thus, this classification-type selection does not apply.
* Object Detection Locates Content in an Image - YESThis statement is true. The object detection functionality in Custom Vision is designed to both identify what objects appear in an image and determine their location through bounding box coordinates. For example, a model could detect and locate multiple products on a store shelf. Microsoft documentation describes object detection as
"identifying the presence and location of objects in an image."
* Predefined Domains - YESThis statement is true as well. When you create a new Custom Vision project, you must select a domain, which is a predefined optimization setting tailored to specific use cases such as retail, food, landmarks, or general images. These domains are designed to improve model accuracy by applying specialized transfer learning features based on the type of images you will analyze.
In summary:
* Classification type (Multilabel/Multiclass): No (only for classification models)
* Detect object location: Yes
* Choose predefined domain: Yes
사진 속의 거리 표지판을 바탕으로 거리 이름을 식별해야 합니다.
어떤 유형의 컴퓨터 비전을 사용해야 합니까?
- A. 객체 감지
- B. 얼굴 인식
- C. 이미지 분류
- D. 광학 문자 인식(OCR)
Correct Answer: D 🗳️
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문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and official Microsoft Learn modules under "Describe features of common AI workloads", Conversational AI refers to technology that enables computers to engage in dialogue or conversation with users through natural language, whether by text or speech. The interactive answering of user-entered questions through a chat interface or virtual assistant is a direct example of a conversational AI workload.
Microsoft defines Conversational AI as systems that use natural language processing (NLP) and language understanding models to interpret what users are asking and respond appropriately. This includes chatbots, virtual assistants (like Cortana or Azure Bot Service), and automated customer service systems that simulate a human-like conversation. In this case, when an application answers questions that a user types interactively, the AI model is processing human language inputs, deriving intent, and generating meaningful replies - precisely what conversational AI is designed to do.
By contrast:
* Anomaly detection identifies unusual patterns in data, typically used for fraud detection or equipment monitoring - not interactive dialogue.
* Computer vision deals with interpreting images or video (e.g., object detection, facial recognition), unrelated to answering text-based questions.
* Forecasting uses historical data to predict future trends or outcomes, often in sales or demand prediction scenarios.
The AI-900 guide emphasizes that Conversational AI helps businesses improve customer interaction efficiency by offering instant, automated, and consistent responses. It enables real-time engagement 24/7 and integrates with tools such as Azure Bot Service, Azure Cognitive Service for Language, and QnA Maker (now part of Azure AI Language Service).
Therefore, based on the Microsoft Learn objectives and definitions from the official AI-900 curriculum, the interactive answering of user questions in an application is best categorized as Conversational AI.
생성적 AI 솔루션의 두 가지 일반적인 사용 사례는 무엇입니까? 각 정답은 완전한 솔루션을 제시합니다.
참고: 정답 1개당 1점입니다.
- A. 과거 데이터를 기반으로 매출 수익 예측
- B. 고객 서비스 담당자를 위한 초안 응답 생성
- C. 이메일 메시지를 스팸 또는 비스팸으로 분류
- D. 텍스트 설명에서 원본 아트워크 만들기
Correct Answer: B,D 🗳️
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분할 데이터 모듈이 포함된 Azure Machine Learning 파이프라인이 있습니다. 분할 데이터 모듈은 모델 학습 모듈과 모델 점수 매기기 모듈로 출력됩니다. 분할 데이터 모듈의 기능은 무엇인가요?
- A. 숫자 변수의 크기를 조정하여 일관된 숫자 범위 내에 있도록 합니다.
- B. 데이터가 누락된 레코드를 전환합니다.
- C. 모델에 포함되어야 하는 열 선택
- D. 학습 및 검증 데이터 세트 생성
Correct Answer: D 🗳️
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