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질문 # 112
Stating the source of the data used to train a model is an example of which responsible AI principle?
정답：A
설명：
Transparency in AI refers to openly sharing information about how an AI system is designed, trained, and operates. Stating the source of the data used to train a model is an example of transparency, as it provides clarity about the origins of the data and helps stakeholders understand the model's development process.
질문 # 113
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 114
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
정답：B
설명：
The REST API request contains an image-generation style payload:
{
" prompt " : " A polar bear on the beach holding an icecream " ,
" size " : " 1024x1024 " ,
" n " : 1,
" style " : " vivid "
}
This request asks the model to generate an image from a text prompt. In Azure OpenAI, DALL-E models are used for image generation from natural language prompts.
Why the other options are incorrect:
B). Codex is associated with code generation tasks.
C). GPT-4.1 is primarily used for text/chat and multimodal reasoning, not this classic image-generation API payload.
D). Whisper is used for speech-to-text transcription.
질문 # 115
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Box 1: Yes
Yes - Whisper models in Azure OpenAI Service are used to convert speech to text.
The Whisper model is a speech to text model from OpenAI that you can use to transcribe or translate audio files. The model is trained on a large dataset of English audio and text. The model is optimized for transcribing audio files that contain speech in English.
Box 2: Yes
Yes - Embedding models in Azure OpenAI Service are used to convert text into numeric vectors.
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
Embeddings: a set of models that can convert text into numerical vector form to facilitate text similarity.
Box 3: No
GPT-4.1 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-4.1 family is GPT-4.1 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-4.1 Turbo is available for use with the Chat Completions API.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
질문 # 116
You need to build an AI solution that produces new product images based on written descriptions provided by users. Which AI workload should you use?
정답：B
설명：
An AI workload that creates entirely new visual content based on written natural language descriptions is defined as image generation, which is a core capability of Generative AI. In the Microsoft Azure ecosystem, this task is primarily handled by generative models like DALL-E or GPT-image via Azure OpenAI Service and Azure AI Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/ai-services/image-video-processing
질문 # 117
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Microsoft AI-901인증시험이 이토록 인기가 많으니 우리Itcertkr에서는 모든 힘을 다하여 여러분이 응시에 도움을 드리겠으며 또 일년무료 업뎃서비스를 제공하며, Itcertkr 선택으로 여러분은 자신의 꿈과 더 가까워질 수 있습니다. 희망찬 내일을 위하여 Itcertkr선택은 정답입니다. Itcertkr선택함으로 당신이 바로 진정한IT인사입니다.
AI-901최신 덤프자료: https://www.itcertkr.com/AI-901_exam.html