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 19, 2026
  • Q & A: 132 Questions and Answers

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Microsoft AI-901 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901

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Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:

Question #1

여러분은 자연어 처리(NLP)를 사용하여 사용자의 텍스트 입력에 따라 다음과 같은 작업을 수행하는 챗봇을 구축하고 있습니다.
* 고객 주문을 받습니다
* 지원 문서 검색
* 주문 상태 업데이트를 조회합니다.
어떤 유형의 자연어 처리(NLP)를 사용해야 할까요?

  • A. 언어 이해
  • B. 감정 분석
  • C. 개체명 인식
  • D. 번역
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

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.

Reveal Solution  Discussion  0

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

Question #3

다음 중 마이크로소프트의 책임 있는 AI 원칙의 예시는 무엇입니까?

  • A. AI 시스템은 이해하기 쉬워야 합니다.
  • B. AI 시스템은 개인 정보를 공개해야 합니다.
  • C. AI 시스템은 공개적으로 이용 가능한 데이터만 사용해야 합니다.
  • D. AI 시스템은 회사의 이익을 보호해야 합니다.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '참고'를 선택하십시오. 각 정답은 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.

Question #5

문장을 올바르게 완성하는 답을 고르시오.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous.
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.

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