AI-900-KR 무료 덤프문제 온라인 액세스

여러분은 소매점의 이미지를 처리하고 경쟁사의 제품을 식별하는 도구를 만들고 있습니다.
해당 솔루션에서는 사용자 정의 모델을 사용합니다.
어떤 Azure Cognitive Services 서비스를 사용해야 하나요?

• A.얼굴
• B.사용자 정의 비전
• C.양식 인식기
• D.컴퓨터 비전

문장을 올바르게 완성하는 답을 선택하세요.

정답:

Explanation:

The question describes a process where an AI system generates text that describes an image - for example,
"A dog playing with a ball in the park." This process is an example of image classification, a core workload in computer vision that allows a system to recognize and categorize the content of an image.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," image classification involves analyzing the pixels of an image and assigning one or more predefined categories or labels to it. In more advanced implementations, image classification models are combined with caption generation algorithms to produce descriptive text. For example, Azure AI Vision can generate captions and tags that describe an image's content, such as "outdoor scene," "a person riding a bicycle," or "a group of people smiling." Let's review the other options to clarify why they are incorrect:
* Facial detection: Identifies the presence and location of human faces in an image, but does not generate descriptive text.
* Object detection: Identifies and locates multiple objects within an image by drawing bounding boxes, not by describing the overall scene.
* Optical character recognition (OCR): Extracts text from images or scanned documents (for example, reading a street sign), but it doesn't create descriptive language about what's depicted.
Therefore, the correct answer is Image classification, as it aligns with the AI-900 learning objective that describes this task as recognizing and categorizing the main content of an image, often leading to caption generation in modern vision models such as those in Azure AI Vision.

문장을 올바르게 완성하는 답을 선택하세요.

정답:

Explanation:

This question refers to a system that monitors a user's emotions or expressions-in this case, identifying whether a kiosk user is annoyed-through a video feed. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," this scenario falls under facial analysis, which is a capability of Azure AI Vision or the Face API.
Facial analysis involves detecting human faces in images or video and analyzing facial features to interpret emotions, expressions, age, gender, or facial landmarks. The AI model does not try to identify who the person is but rather interprets how they appear or feel. For example, facial analysis can detect emotions such as happiness, anger, sadness, or surprise, which allows applications to infer a user's engagement or frustration level while interacting with a system.
Option review:
* Face detection: Identifies the presence and location of a face in an image but does not interpret expressions or emotions.
* Facial recognition: Matches a detected face to a known individual's identity (for authentication or security), not for emotion detection.
* Optical character recognition (OCR): Extracts text from images or scanned documents and has no relation to human emotion or facial features.
Therefore, determining whether a kiosk user is annoyed, happy, or frustrated involves emotion detection within facial analysis, making Facial analysis the correct answer.
This aligns with AI-900's definition of computer vision workloads, where facial analysis provides insights into emotions and expressions, supporting user experience optimization and customer behavior analytics.

개인 디지털 사진 컬렉션에 레이블을 지정하는 모델을 만들어야 합니다.
어떤 Azure AI 서비스를 사용해야 하나요?

• A.Azure AI 컴퓨터 비전
• B.Azure AI 문서 인텔리전스
• C.Azure Al 언어
• D.Azure Al Custom Vision

Azure OpenAI DALL-E 모델을 사용하여 수행할 수 있는 두 가지 작업은 무엇입니까? 각 정답은 완전한 해결책을 제시합니다.
참고: 정답은 1점입니다.

• A.이미지를 생성합니다.
• B.이미지를 수정합니다.
• C.광학 문자 인식(OCR)을 사용합니다.
• D.이미지에 대한 캡션을 생성합니다.
• E.이미지에서 객체를 감지합니다.