Microsoft AI-901 Korean Exam : Microsoft Azure AI Fundamentals (AI-901 Korean Version)
- Exam Code: AI-901-KR
- Exam Name: Microsoft Azure AI Fundamentals (AI-901 Korean Version)
- Updated: Oct 22, 2026
- Q & A: 132 Questions and Answers
About Microsoft AI-901 Korean Exam Questions
A company that researches study materials for years develops instincts newcomers lack. The DumpsActual experts know which Microsoft Azure AI Fundamentals (AI-901 Korean Version) topics trip candidates up, and the 132 practice questions are weighted accordingly.
Microsoft AI-901 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Describe core machine learning concepts
|
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify features of common NLP workload scenarios
|
| Topic 3: Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify generative AI services and capabilities in Microsoft Azure
|
| Topic 4: Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for computer vision tasks
|
| Topic 5: Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify guiding principles for responsible AI
|
AI-901 Korean Exam FAQ — Methods, Measurement, Mastery
The Microsoft Azure AI Fundamentals (AI-901 Korean Version) exam contains 40-60 to complete within 45-60. Timed simulation in the DumpsActual Desktop Test Engine teaches that rhythm before exam day does.
The official Microsoft Azure AI Fundamentals (AI-901 Korean Version) outline defines 5 domains. The leading three are Describe Artificial Intelligence workloads and considerations (15-20%) (15-20%), Describe fundamental principles of machine learning on Azure (15-20%) (15-20%), and Describe features of generative AI workloads on Azure (20-25%) (20-25%). The full list is in the topics section above, and DumpsActual's 132 practice questions cover each one.
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The AI-901 Korean exam is a Microsoft certification exam validating the Microsoft Azure AI Fundamentals (AI-901 Korean Version) syllabus shown above. It belongs to these credential paths: Microsoft Certified: Azure AI Fundamentals. Candidates in 2026 start with DumpsActual's 132 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
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Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:
Microsoft Foundry 프로젝트인 project1이 있고, 이 프로젝트 안에 Resource1이라는 Azure OpenAI 리소스가 있습니다.
Resource1에 my-mini-gpt라는 이름의 모델 배포를 사용하여 gpt-4.1-mini 모델을 배포합니다.
애플리케이션에서 my-mini-gpt에 연결해야 합니다.
파이썬 코드를 어떻게 완성해야 할까요? 정답을 맞추려면 적절한 값을 해당 위치로 드래그하세요.
각 값은 한 번 사용될 수도 있고, 여러 번 사용될 수도 있으며, 전혀 사용되지 않을 수도 있습니다.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
client = OpenAI(
api_key= " ... " ,
base_url= " https://resource1.openai.azure.com/openai/v1/ " ,
)
response = client.responses.create(
model= " my-mini-gpt " ,
)
For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:
https:// < resource-name > .openai.azure.com/openai/v1/
In the question, the Azure OpenAI resource is named Resource1, so the first blank must be resource1.
Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.
azure.com/openai/v1/ path.
For the model parameter, Azure OpenAI requires the deployment name, not the underlying model name.
Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.
The deployed model is gpt-4.1-mini, but the deployment name is my-mini-gpt. Therefore, the second blank must be:
model= " my-mini-gpt "
So the correct selections are:
base_url blank = resource1
model blank = my-mini-gpt
당신은 인공지능 기반 고객 지원 애플리케이션을 개발하고 있습니다.
다음 작업 중 마이크로소프트의 책임 있는 AI 원칙인 포용성을 보여주는 예는 무엇입니까?
- A. 저장된 고객 데이터를 암호화하고 역할 기반 제어를 사용하여 접근을 제한합니다.
- B. 여러 언어와 화면 낭독기를 지원하는 인터페이스를 설계합니다.
- C. 편향을 줄이기 위해 인구통계학적 그룹별로 모델 결과를 평가합니다.
- D. 예측이 어떻게 생성되는지 설명하십시오.
Correct Answer: B 🗳️
Explanation: Only visible for DumpsActual members. You can sign-up / login (it's free).
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:
Explanation:
Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:
Explanation:
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.
Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.
The other options are incorrect:
Key phrase extraction identifies important phrases or main topics.
Language detection identifies the language of the text.
Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:
Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.
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