20+ practice questions focused on Describe features of computer vision workloads on Azure — one of the most tested topics on the Microsoft Azure AI Fundamentals AI-900 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Describe features of computer vision workloads on Azure PracticeA museum wants to create an application that automatically generates descriptive captions for uploaded photos of artworks. The captions should describe the main subject, scene, and artistic style. Which Azure Computer Vision capability should they use?
Explanation: The Image Analysis capability in Azure Computer Vision includes a description feature that generates human-readable captions summarizing the main subject and scene of an image, using pre-trained deep learning models. It does not explicitly identify artistic style, but it is the only option that provides automated image captioning without custom training.
A social media platform wants to automatically generate a textual description for each user-uploaded image to assist visually impaired users. Which prebuilt Azure Computer Vision feature should they use?
Explanation: Azure AI Vision's Image Analysis API provides an image captioning feature that generates a human-readable sentence describing the entire image. Object detection returns object labels and bounding boxes, OCR extracts text, and face detection identifies faces, none of which produce a full textual description.
A retail company wants to use Azure AI services to monitor shelf inventory. They need to detect whether specific products (e.g., 'Brand A cereal', 'Brand B cereal') are present on a shelf and count the number of units of each product. They have a labeled dataset with images of each product category. Which Azure AI capability should they use?
Explanation: The correct capability is custom object detection, which is offered by Azure Custom Vision. You cannot train Azure Computer Vision to detect specific product categories; prebuilt Computer Vision only supports common objects and generic image analysis. To detect and count specific products like 'Brand A cereal', you need to train a custom model using Azure Custom Vision. Therefore, the intended answer (A) is correct in spirit, but it must be clarified that this is a Custom Vision capability, not a Computer Vision capability.
A manufacturing company wants to use Azure Computer Vision to automatically inspect products on an assembly line for defects. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capabilities could be used together to achieve this? (Select two options.)
Explanation: Object Detection in Azure Computer Vision can identify and locate multiple specific defect types (e.g., scratch, dent, crack) within product images by drawing bounding boxes around each defect. Semantic Segmentation goes further by classifying each pixel, enabling precise localization and classification of defects at the pixel level. Together, these capabilities allow the system to both identify defect types and pinpoint their exact locations on the assembly line.
A security company needs to identify individuals in a crowd by matching their faces against a database of known persons of interest. The system must detect faces, verify the identities, and provide a confidence score. Which Azure AI capability should they use?
Explanation: Azure Face service (Face API) is the correct capability for detecting faces, verifying identities against a database, and returning confidence scores. Azure Computer Vision only performs face detection (locating faces), not identity matching.
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