AI-900시험대비최신버전덤프자료, AI-900시험패스가능한공부

그 외, Itexamdump AI-900 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1Pu-KsQdehD_SnCVPPP9ba_Jw4jVNB7UZ

Microsoft 인증 AI-900시험이 너무 어려워서 시험 볼 엄두도 나지 않는다구요? Itexamdump 덤프만 공부하신다면 IT인증시험공부고민은 이젠 그만 하셔도 됩니다. Itexamdump에서 제공해드리는Microsoft 인증 AI-900시험대비 덤프는 덤프제공사이트에서 가장 최신버전이여서 시험패스는 한방에 갑니다. Microsoft 인증 AI-900시험뿐만 아니라 IT인증시험에 관한 모든 시험에 대비한 덤프를 제공해드립니다. 많은 애용 바랍니다.

Microsoft AI-900 인증 시험은 인공 지능 기술을 개발하고 Azure 플랫폼에서 응용 프로그램을 탐색하려는 개인을위한 견고한 토대를 제공합니다. 이 인증을 받음으로써 후보자는 잠재적 인 고용주에게 전문 지식을 보여주고 빠르게 성장하는 AI 분야에서 경력 발전 기회를 열 수 있습니다.

>> AI-900시험대비 최신버전 덤프자료 <<

100% 합격보장 가능한 AI-900시험대비 최신버전 덤프자료 공부자료

Microsoft AI-900시험은 Itexamdump 에서 출시한Microsoft AI-900덤프로 도전하시면 됩니다. Microsoft AI-900 덤프를 페펙트하게 공부하시면 시험을 한번에 패스할수 있습니다. 구매후 일년무료 업데이트 서비스를 제공해드리기에Microsoft AI-900시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다.

이 시험은 40~60개의 객관식 문제로 이루어져 있으며, 60분의 시간 제한이 있습니다. 시험을 통과하려면, 후보자는 1000점 중 700점 이상의 최소 점수를 달성해야 합니다. 이 시험은 온라인으로 또는 테스트 센터를 통해 볼 수 있으며, 시험 비용은 장소에 따라 다릅니다.

최신 Microsoft Certified: Azure AI Fundamentals AI-900 무료샘플문제 (Q173-Q178):

질문 # 173
To complete the sentence, select the appropriate option in the answer area.

정답：

설명：

Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/


질문 # 174
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?

정답：A

설명：
Explanation
Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr


질문 # 175
You have a webchat bot that provides responses from a QnA Maker knowledge base.
You need to ensure that the bot uses user feedback to improve the relevance of the responses over time.
What should you use?

정답：C

설명：
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and the official Microsoft Learn module "Describe features of common AI workloads", QnA Maker (now part of Azure AI Language services) allows developers to build, train, and publish a knowledge base that provides natural-language answers to user queries. A key capability of this service is active learning, which enables the knowledge base to automatically suggest improvements by analyzing user feedback and usage patterns.
Active learning is an iterative process in which the service observes real user interactions and identifies ambiguous questions or pairs of similar questions that produce uncertain or multiple answers. The system then recommends updates or refinements to the knowledge base to improve the accuracy and relevance of responses. This feedback loop helps ensure that over time, the chatbot's responses align more closely with actual user expectations and language variations.
In contrast:
* A. Key phrase extraction identifies main ideas in text and is used in content summarization, not in response optimization.
* B. Sentiment analysis detects emotional tone (positive, negative, neutral), but it doesn't refine QnA responses.
* C. Business logic defines operational rules in an application, not machine learning-driven feedback.
The AI-900 guide specifically emphasizes that QnA Maker supports active learning to improve the quality of answers based on end-user feedback, making this the verified and official Microsoft answer.
Reference (from Microsoft Learn AI-900 content):
"Active learning uses feedback from end users to automatically suggest improvements to a knowledge base, helping improve the accuracy of answers over time."


질문 # 176
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
A screenshot of a computer Description automatically generated with medium confidence

Box 1: Yes
Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.
Box 2: Yes
The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.
Box 3: No
Custom Vision service can be used only on graphic files.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview


질문 # 177
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 # 178
......

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