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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
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Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:
생성형 AI 모델에 명령어를 입력할 때의 두 가지 목적은 무엇인가요? 각 정답은 해답의 일부를 나타냅니다.
참고: 정답 하나당 1점입니다.
- A. 모델에 대한 분당 토큰(TPM) 할당량을 정의합니다.
- B. 사용할 모델을 선택합니다.
- C. 모델의 응답에 대한 제약 조건을 정의합니다.
- D. 추론이 발생하는 Azure 지역을 정의합니다.
- E. 에이전트의 역할과 행동을 정의합니다.
Correct Answer: C,E 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
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.
귀사는 인공지능(AI) 활용을 위한 모범 사례를 검토하고 있습니다.
각 작업은 마이크로소프트의 책임 있는 AI 원칙 중 어떤 원칙에 해당하는 예시인가요? 정답을 맞추려면 해당 원칙을 올바른 작업으로 드래그하세요. 각 작업은 한 번, 여러 번 또는 전혀 사용되지 않을 수 있습니다. 내용을 보려면 창 사이의 분할 막대를 드래그하거나 스크롤해야 할 수도 있습니다.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
Fairness
Privacy and security
Transparency
Reliability and safety
Comprehensive and Detailed Explanation with all Azure AI documents: =
Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
Fairness
Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.
채용 결정을 지원하기 위해 AI 시스템을 도입합니다.
회사 정책에 따라 사람이 AI가 생성한 추천 사항을 검토하고 최종 채용 결정에 대한 책임을 져야 합니다.
이것은 마이크로소프트의 어떤 AI 원칙의 예시인가요?
- A. 투명성
- B. 책임성
- C. 공정성
- D. 신뢰성 및 안전성
Correct Answer: B 🗳️
문장을 올바르게 완성하는 답을 고르시오.
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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