AI-900 무료 덤프문제 온라인 액세스

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

정답:

Explanation:
facial analysis.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of computer vision workloads on Azure," facial analysis is a computer vision capability that detects faces and extracts attributes such as facial expressions, emotions, pose, occlusion, and image quality factors like exposure and noise. It does not identify or verify individual identities; rather, it interprets facial features and image characteristics to analyze conditions in an image.
In this question, the AI solution helps photographers take better portrait photos by providing feedback on exposure, noise, and occlusion - tasks directly linked to facial analysis. The model analyzes the detected face to determine if the image is well-lit, clear, and unobstructed, thereby improving photo quality. These capabilities are part of the Azure Face service in Azure Cognitive Services, which includes both facial detection and facial analysis functionalities.
Here's how the other options differ:
* Facial detection only identifies that a face exists in an image and provides its location using bounding boxes, without further interpretation.
* Facial recognition goes a step further - it attempts to identify or verify a person's identity by comparing the detected face with stored images. This is not what the scenario describes.
Thus, when an AI solution evaluates image quality aspects like exposure, noise, and occlusion, it's performing facial analysis, which focuses on understanding image and facial characteristics rather than identification.
In summary, based on Microsoft's AI-900 study material, this scenario demonstrates facial analysis, a subcategory of computer vision tasks within Azure Cognitive Services.

Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

• A.Azure Al Document Intelligence
• B.Azure Al Language
• C.Azure Al face
• D.Azure Al Custom Vision

You have the process shown in the following exhibit.

Which type AI solution is shown in the diagram?

• A.a sentiment analysis solution
• B.a machine learning model
• C.a computer vision application
• D.a chatbot

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:

Explanation:

The correct answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning." In supervised machine learning, data is typically divided into three main subsets:
* Training set - used to train the model, i.e., to teach the algorithm the patterns and relationships between input features and output labels.
* Validation set - used to evaluate the model during training to tune hyperparameters and prevent overfitting.
* Test set - used after training to assess the final model's performance on unseen data.
Let's analyze each statement in light of these definitions:
* "A validation set includes the set of input examples that will be used to train a model." # NoThis is incorrect because the training set, not the validation set, contains the input examples used for model training. The validation set is separate from the training data to ensure unbiased evaluation.
* "A validation set can be used to determine how well a model predicts labels." # YesThis is correct. The validation set helps assess how effectively the model generalizes during training. It measures performance and helps tune model parameters for optimal results.
* "A validation set can be used to verify that all the training data was used to train the model." # NoThis is false. The validation set is not used to verify the completeness of training data usage. It exists independently to evaluate the model's performance during training cycles.
According to Microsoft Learn, using a validation set helps ensure that a model generalizes well and avoids overfitting to the training data. It plays a crucial role in refining and optimizing models before final testing.

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:

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.