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Microsoft AI-900 Korean Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-900
Real Exam Qty:40–60
Exam Price:$99 USD
Related Certifications:Microsoft Certified: Azure Data Fundamentals
Microsoft Certified: Azure Fundamentals
Certificate Validity Period:Valid indefinitely
Exam Duration:45 minutes
Available Languages:Arabic (Saudi Arabia), Indonesian (Indonesia), Portuguese (Brazil), Japanese, Spanish, German, Chinese (Simplified), Chinese (Traditional), Italian, Russian, French, Korean, English
Exam Format:Multiple choice, Scenario-based questions, Multiple select
Passing Score:700 (on a scale of 1–1000)
Recommended Training:Instructor-led Training: AI-900 Course
Microsoft Learn: Azure AI Fundamentals Learning Path
Exam Registration:Microsoft Certification Registration
Pearson VUE Registration
Sample Questions:Microsoft AI-900 Korean Sample Questions
Exam Way:Online proctored or onsite testing at Pearson VUE test centers
Pre Condition:No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/

Microsoft AI-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Speech
- Describe capabilities of Azure Translator
- Describe capabilities of Azure Language
- Identify types of NLP solutions
Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe considerations for developing AI solutions
- Describe responsible AI principles
Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Face
- Describe capabilities of Azure Form Recognizer
- Identify types of computer vision solutions
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Computer Vision
Fundamental principles of machine learning on Azure15–20%- Describe automated machine learning
- Describe capabilities of Azure Machine Learning
- Describe core concepts of machine learning
- Describe machine learning pipelines
Features of generative AI workloads on Azure20–25%- Describe generative AI concepts
- Describe use cases for generative AI
- Describe responsible AI practices for generative AI
- Describe capabilities of Azure OpenAI Service

AI-900 Korean Exam FAQ: What Candidates Ask Most

The Microsoft AI-900 Korean exam, officially titled Microsoft Azure AI Fundamentals (AI-900 Korean Version), is the required test for earning the Microsoft Certified: Azure AI Fundamentals certification, a credential at the Fundamental level. Passing it validates the skills Microsoft expects from certified professionals, and it can also support progress toward related credentials such as Microsoft Certified: Azure Data Fundamentals, Microsoft Certified: Azure Fundamentals.

The AI-900 Korean exam includes 40–60 questions, and you have 45 minutes to complete it. Before exam day, divide the available time by the question count so you know the pace you need to hold, and practice flagging time-consuming items for review instead of stalling on a single question. Timed sessions in the Actualtests4sure test engine are the easiest way to build that rhythm before it counts.

You need 700 (on a scale of 1–1000) to pass the AI-900 Korean exam, and the official registration fee is $99 USD. Keep in mind that a failed attempt means paying that fee in full again for a retake, so avoid booking your seat on a hunch. Work through the Actualtests4sure practice test until your scores sit comfortably above the passing requirement before you schedule the exam.

Microsoft asks candidates to meet the following requirement before registering: No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory. Exam policies do change, so confirm the latest details on the official exam page at https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/ before you book.

You can book your seat through the official registration channels below:

The AI-900 Korean exam is delivered in the following format: Online proctored or onsite testing at Pearson VUE test centers.

Microsoft recommends the following official training for this exam:

A course builds the theory; practice turns it into exam-day performance. Once you finish a class, the 336 practice questions from Actualtests4sure show you how the same knowledge appears in exam-style items.

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Every Actualtests4sure order is covered by a 100% Money Back Guarantee. If you take the corresponding AI-900 Korean exam within 60 days of purchase and do not pass, send a scan of your exam enrollment slip together with your official Score Report PDF within two days of the exam date, and your claim will be processed within seven days. The candidate name must match the payer name, and the guarantee does not apply if you take the exam within three days of purchase, if you downloaded the product but never took the exam, or to free materials and expired orders. If you would rather have fresh material than a refund, you can exchange your purchase for two additional exam products of equal value at no cost and keep the update service on your original product. Delivery itself is instant: your download is available right after payment and a copy is emailed to you within one minute — if nothing arrives within two hours, contact our support team. You may install the software on as many computers as you need.

The Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam is organized into 5 major domains. Some of the key domains include:

  • Features of computer vision workloads on Azure, accounting for 15–20% of the exam
  • Features of generative AI workloads on Azure, accounting for 20–25% of the exam
  • Fundamental principles of machine learning on Azure, accounting for 15–20% of the exam

Scroll up to the Exam Topics section for the complete breakdown, and use it to plan how much study time each domain deserves.

Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:

Question #1

머신 러닝 분류 모델에서 생성되는 거짓 양성의 수를 줄이려면 어떻게 해야 합니까?

  • A. 거짓 양성을 선호하도록 임계값을 수정합니다.
  • B. 거짓 부정을 선호하도록 임계값을 수정합니다.
  • C. 훈련 데이터에 테스트 데이터를 포함합니다.
  • D. 학습 반복 횟수를 늘립니다.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

The correct answers are Yes, Yes, and Yes.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn content in the section "Describe features of conversational AI workloads on Azure", bots created using Azure Bot Service can interact with users across multiple channels. The AI-900 syllabus explains that Azure Bot Service integrates with various communication platforms, allowing developers to build a single bot that can be deployed in many contexts without rewriting the logic.
* "You can communicate with a bot by using Cortana." - Yes.The AI-900 learning materials explain that Cortana, Microsoft's intelligent personal assistant, can serve as a channel for bots built with the Azure Bot Service. Through the Bot Framework, bots can be connected to Cortana to allow users to interact via voice or text. Although Cortana is less prominent now, it remains conceptually included in the AI-
900 coverage as an example of a voice-based conversational AI channel.
* "You can communicate with a bot by using Microsoft Teams." - Yes.This statement is true and directly referenced in the AI-900 syllabus. Microsoft Teams is a fully supported communication channel for Azure Bot Service. Bots in Teams can handle chat messages, commands, and interactions in team or personal contexts. The Microsoft Learn materials specify Teams as one of the native connectors where enterprise users can interact with organizational bots.
* "You can communicate with a bot by using a webchat interface." - Yes.This is also true. The Web Chat channel is one of the most common ways to deploy bots publicly. Azure Bot Service provides a Web Chat control that can be embedded directly into a webpage or web application. This allows users to interact with the bot using a chat window, just like on customer service websites.
Therefore, all three interfaces-Cortana (voice-based), Microsoft Teams (enterprise chat), and Web Chat (browser-based)-are valid and officially supported communication channels for Azure bots.

Question #3

Azure에서 자연어 처리 솔루션을 개발하고 있습니다. 이 솔루션은 고객 리뷰를 분석하여 각 리뷰의 긍정적 또는 부정적 정도를 판단합니다.
이는 어떤 유형의 자연어 처리 작업 부하의 예입니까?

  • A. 언어 감지
  • B. 감정 분석
  • C. 핵심어 추출
  • D. 엔티티 인식
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

자동차 판매 데이터가 포함된 대규모 데이터 세트가 있습니다.
차량 유형에 따라 차량 판매 가치를 예측하려면 자동화된 머신 러닝(자동화 ML) 모델을 학습시켜야 합니다.
어떤 작업을 선택해야 할까요? 답변하려면 답변 영역에서 적절한 작업을 선택하세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning documentation, regression is the appropriate machine learning task when the goal is to predict continuous numeric values- such as prices, sales amounts, or other measurable quantities.
In this scenario, the dataset contains motor vehicle sales data, and the objective is to predict vehicle sale values. A vehicle's sale value (price) is a continuous numeric variable, meaning it can take on a wide range of possible numbers (for example, $15,000, $28,500, $42,300). Regression models are designed to analyze relationships between input features (like make, model, mileage, age, fuel type) and a continuous output variable (price).
Automated Machine Learning (AutoML) in Azure simplifies this process by automatically testing multiple regression algorithms (e.g., Linear Regression, Random Forest, Gradient Boosted Trees) and hyperparameters to find the best-performing model.
Let's evaluate why the other options are incorrect:
* Classification - Used for predicting discrete categories (e.g., car type: sedan, SUV, or truck). Sale value prediction is not categorical.
* Time series forecasting - Used when predicting future values based on time-dependent data (e.g., sales over months or years). The question focuses on predicting price based on vehicle characteristics, not over time.
* Natural Language Processing (NLP) - Deals with text-based data, not numeric vehicle data.
* Computer Vision - Applies to image-based tasks (e.g., detecting car types from photos).
Therefore, per Microsoft Learn's Automated ML task selection guidance, when predicting a numeric output like vehicle sale value, the correct machine learning task type is Regression.
# Final answer: Regression

Question #5

디지털 사진에서 유명 브랜드를 식별하는 사전 구축된 솔루션을 구현해야 합니다. 어떤 Azure Alsen/Tee를 사용해야 할까요?

  • A. 얼굴
  • B. 양식 인식기
  • C. 맞춤형 비전
  • D. 컴퓨터 비전
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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