BONUS!!! Fast2test AI-900 시험 문제집 전체 버전을 무료로 다운로드하세요: https://drive.google.com/open?id=1AdXjL83g3r3GldFcBL1TkMmYSj9Qz1c2
다년간 IT업계에 종사하신 전문가들이 자신의 노하우와 경험으로 제작한 Microsoft AI-900덤프는 AI-900 실제 기출문제를 기반으로 한 자료로서 AI-900시험문제의 모든 범위와 유형을 포함하고 있어 높을 적중율을 자랑하고 있습니다.덤프구매후 불합격 받으시면 구매일로부터 60일내 주문은 덤프비용을 환불해드립니다.IT 자격증 취득은 Fast2test덤프가 정답입니다.
| Section | Weight | Objectives |
|---|---|---|
| Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe core machine learning concepts - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities |
Fast2test 는 여러분의 it전문가 꿈을 이루어드리는 사이트 입다. Fast2test는 여러분이 우리 자료로 관심 가는 인중시험에 응시하여 안전하게 자격증을 취득할 수 있도록 도와드립니다. 아직도Microsoft AI-900인증시험으로 고민하시고 계십니까?Microsoft AI-900인증시험가이드를 사용하실 생각은 없나요? Fast2test는 여러분에 편리를 드릴 수 잇습니다. Fast2test의 자료는 시험대비최고의 덤프로 시험패스는 문제없습니다. Fast2test의 각종인증시험자료는 모두기출문제와 같은 것으로 덤프보고 시험패스는 문제없습니다. Fast2test의 퍼펙트한 덤프인 M crosoftAI-900인증시험자료의 문제와 답만 열심히 공부하면 여러분은 완전 안전히Microsoft AI-900인증자격증을 취득하실 수 있습니다.
질문 # 30
To complete the sentence, select the appropriate option in the answer area.
정답：
설명：
Explanation:
Table Description automatically generated with medium confidence
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
질문 # 31
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.
* "You train a regression model by using unlabeled data." - No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.
* "The classification technique is used to predict sequential numerical data over time." - No.
Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam
/not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).
* "Grouping items by their common characteristics is an example of clustering." - Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of "discovering natural groupings in data without predefined labels." Common examples include customer segmentation or document grouping.
Therefore, based on Microsoft's AI-900 training objectives and definitions:
* Regression # supervised learning using labeled continuous data (No)
* Classification # categorical prediction, not sequential numeric forecasting (No)
* Clustering # grouping by similarity (Yes)
질문 # 32
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
정답：A,C
설명：
You can drag-and-drop datasets and modules onto the canvas.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
질문 # 33
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
Graphical user interface, text, application Description automatically generated
질문 # 34
You have the following apps:
* App1: Uses a set of images and photos to extract brand names
* App2: Enables touchless access control for buildings
Which Azure Al Vision service does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure AI Vision services, different Azure AI Vision capabilities are suited for different use cases such as object detection, brand recognition, facial recognition, and spatial analysis.
* App1: Uses a set of images and photos to extract brand names # Image AnalysisThe Azure AI Vision - Image Analysis service (formerly part of Computer Vision) can detect and extract brands, objects, text, and other visual features from images. It uses advanced image classification and object detection models to recognize logos and identify brand names (for example, "Microsoft" or "Coca-Cola") in photos. The Image Analysis API can also return descriptive tags, scene descriptions, and confidence scores. Therefore, since App1 analyzes static images to extract brand names, it specifically relies on the Image Analysis feature of Azure AI Vision.
* App2: Enables touchless access control for buildings # FaceThe Azure AI Face service is designed for facial detection, verification, and identification. It can recognize and match faces in real time, making it ideal for access control, identity verification, and attendance tracking systems. A "touchless access control" system uses a camera to detect a person's face and verify identity against a stored profile, allowing or denying entry without physical interaction.
The other options are not suitable:
* Optical Character Recognition (OCR) extracts text, not brand logos.
* Spatial Analysis is for detecting movement or presence in video feeds.
* Video Analysis is for analyzing dynamic video content rather than still images.
질문 # 35
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Fast2test는Microsoft AI-900시험을 패스할 수 있는 아주 좋은 사이트입니다. Fast2test은 아주 알맞게 최고의Microsoft AI-900시험문제와 답 내용을 만들어 냅니다. 덤프는 기존의 시험문제와 답과 시험문제분석 등입니다. Fast2test에서 제공하는Microsoft AI-900시험자료의 문제와 답은 실제시험의 문제와 답과 아주 비슷합니다.
AI-900완벽한 덤프공부자료: https://kr.fast2test.com/AI-900-premium-file.html
참고: Fast2test에서 Google Drive로 공유하는 무료 2026 Microsoft AI-900 시험 문제집이 있습니다: https://drive.google.com/open?id=1AdXjL83g3r3GldFcBL1TkMmYSj9Qz1c2