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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:
다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니요'를 선택하십시오.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services. = No Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to- speech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This is correct. Microsoft's guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.
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
Azure Language SDK와 Microsoft Foundry 리소스를 사용하는 애플리케이션을 개발하고 있습니다.
비정형 텍스트에서 중요한 주제 목록을 반환해야 합니다.
어떤 파이썬 메서드를 사용해야 할까요?
- A. 클라이언트.begin_extract_summary()
- B. 클라이언트.extract_key_phrases()
- C. client.recognize_linked_entities()
- D. 클라이언트.인식_엔티티()
Correct Answer: B 🗳️
Explanation: Only visible for DumpsKing 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.
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:
Explanation:
Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness.
The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.
The other options are incorrect:
accountability means people and organizations remain responsible for AI systems and their effects.
privacy and security focuses on protecting data and controlling access.
transparency means users should understand when and how AI is being used, including system capabilities and limitations.
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