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 transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?
Explanation: Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.
A manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?
Explanation: Option A is correct because Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.
A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?
Explanation: Optical character recognition (OCR) is the correct Azure Cognitive Services capability because it is specifically designed to extract printed or handwritten text from images and convert it into machine-readable digital text. In this scenario, the logistics company needs to read handwritten addresses from shipping labels, which is a classic OCR workload. Azure's Computer Vision OCR API (including the Read API) can handle both printed and handwritten text, making it the ideal choice for this task.
A logistics warehouse uses a conveyor belt system to move packages. They need to automatically read the alphanumeric serial numbers printed on labels attached to each box. The labels may have different fonts and be somewhat dusty. Which Azure Computer Vision feature should they use?
Explanation: The Read API, part of Azure Computer Vision's OCR capabilities, is specifically designed to extract printed and handwritten text from images, including alphanumeric serial numbers. It can handle varying fonts and degraded image quality (e.g., dusty labels) by using deep-learning models optimized for text recognition. This makes it the correct choice for reading serial numbers from conveyor belt packages.
A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?
Explanation: The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.
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