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Microsoft AI-901 Korean Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:

Explanation:
The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use, which maps directly to the Microsoft responsible AI principle of reliability and safety.
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety.
다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니요'를 선택하십시오.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
Statement 1: The Temperature parameter can be set before deploying a model. = No temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.
Statement 2: During inference, the model name is used to route requests to a specific deployment. = No In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name, even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.
Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.
Foundry Tools 서비스의 Azure Vision을 사용하여 어떤 작업을 수행할 수 있습니까?
- A. 실시간 영상에서 동물의 품종 식별
- B. 교육 영상용 썸네일 제작
- C. 손글씨에서 데이터 추출
- D. 문서에서 핵심 문구 추출
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
Microsoft Foundry 프로젝트에 생성형 AI 모델이 배포된 상태로 존재합니다. Microsoft Foundry SDK를 사용하여 배포된 모델에 채팅 메시지를 보내는 애플리케이션을 개발해야 합니다. 애플리케이션에는 어떤 정보가 포함되어야 합니까?
- A. 파운드리 프로젝트 표시 이름, 모델 학습 데이터셋 및 작업 유형
- B. 모델 학습 데이터셋, 내보낸 플레이그라운드 세션 기록 및 파운드리 프로젝트 표시 이름
- C. 내보낸 플레이그라운드 세션 기록, 엔드포인트 URL 및 작업 유형
- D. 자격 증명, 엔드포인트 URL 및 모델 배포 이름
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
당신은 Foundry Tools의 Azure Language를 사용하여 고객 의견을 요약하는 애플리케이션을 개발하고 있습니다.
Azure Language SDK를 설치해야 합니다.
어떤 파이썬 패키지를 설치해야 할까요?
- A. 랭체인
- B. Azure AI 텍스트 분석
- C. 아주르 AI 번역 텍스트
- D. azure-search-documents
Correct Answer: B 🗳️
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Viewing questions 1-5 out of 132 questions.
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