AI-900 Describe features of computer vision workloads on Azure — Timed 15 Questions

Question 1

A 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?

Accepted Answer

Image classification. 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.

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Object detection

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 2

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?

Accepted Answer

A. Use Custom Vision to train a classification or object detection model with transfer learning. 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.

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B. Use the Optical Character Recognition (OCR) API

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C. Use the Describe Image API (Image Captioning)

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D. Use the Face API

Question 3

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?

Accepted Answer

Optical character recognition (OCR). 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.

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Image classification

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Object detection

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Face detection

Question 4

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?

Accepted Answer

Optical Character Recognition (OCR) using the Read API. 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.

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Image Classification

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Object Detection

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Image Analysis (captioning and tagging)

Question 5

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?

Accepted Answer

Azure Face API. 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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Azure Computer Vision - Image Analysis

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Azure Custom Vision

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Azure Form Recognizer

Question 6

A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)

Question 7

A construction company uses drone images to survey construction sites. They need an automated system that can identify specific types of heavy equipment (e.g., bulldozers, cranes, excavators) in an image and also draw precise pixel-level outlines around each equipment type. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct capability because it assigns a class label (e.g., bulldozer, crane, excavator) to every pixel in the image, producing precise pixel-level outlines around each equipment type. Object detection only provides bounding boxes, not pixel-level masks, while image classification labels the entire image without localization. OCR is irrelevant as it extracts text, not equipment shapes.

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Object detection

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Image classification

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Optical Character Recognition (OCR)

Question 8

A brand monitoring company wants to automatically detect the presence of specific logos (e.g., Apple, Coca-Cola) in social media images. The logos can appear in various orientations and sizes within the image. Which Azure Computer Vision capability is specifically designed to identify popular brands from their logos?

Accepted Answer

Brand Detection. Brand Detection is a specialized Azure Computer Vision capability that uses a pre-trained model to identify thousands of global brands from their logos in images. It is specifically designed to handle variations in logo orientation, size, and placement, making it the correct choice for this scenario.

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Image Classification

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Object Detection

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Optical Character Recognition

Question 9

A 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?

Accepted Answer

Image Analysis (with description feature). Option B is correct because the Image Analysis capability in Azure Computer Vision includes a 'description' feature that generates human-readable captions summarizing the main subject, scene, and artistic style of an image. This is achieved through pre-trained deep learning models that analyze visual content and produce natural language descriptions, making it ideal for automatically captioning artwork photos.

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Optical Character Recognition (OCR)

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Face API

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Custom Vision (object detection)

Question 10

A real estate company wants to create an application that automatically generates floor plans from photographs of rooms. The application needs to identify and delineate every pixel in the image that corresponds to walls, doors, windows, and furniture. Which Azure Computer Vision capability should the company use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image into predefined categories (e.g., walls, doors, windows, furniture), producing a pixel-level mask. This is exactly what the application needs to delineate each structural element and object in the room photograph, enabling accurate floor plan generation.

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Object Detection

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Image Classification

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Optical Character Recognition (OCR)

Question 11

A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract text, key-value pairs, and tables from scanned documents, including invoices with both printed text and handwritten notes. It uses optical character recognition (OCR) combined with deep learning models to handle mixed content, making it the correct choice for this scenario.

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Azure Face API

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Azure Custom Vision

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Azure Video Indexer

Question 12

A quality control manager at a bottling plant needs an automated system to inspect images of bottles coming off the production line. The system must determine whether each bottle has a correctly sealed cap or is defective (cap missing or crooked). The manager has a set of labeled images showing both acceptable and defective bottles. Which Azure Computer Vision service should they use to build a model that classifies each bottle image as 'acceptable' or 'defective'?

Accepted Answer

Azure Custom Vision (Image Classification). Azure Custom Vision (Image Classification) is the correct service because it allows you to upload labeled images of bottles (acceptable and defective) and train a custom image classification model to distinguish between the two classes. This service is specifically designed for scenarios where you need to classify images into user-defined categories without requiring deep learning expertise.

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Azure Face API

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Azure Form Recognizer

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Azure OCR (Read API)

Question 13

A digital art library wants to automatically generate a list of relevant keywords (e.g., 'landscape', 'portrait', 'abstract', 'nature') for each image in their collection. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Tagging. Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 14

A security company wants to monitor a restricted area using camera feeds. The system must detect if a person is present in each video frame and draw a rectangle around each detected person. Which Azure Cognitive Services Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct capability because it identifies and locates objects (including people) within an image by drawing bounding boxes around each detected instance. This directly matches the requirement to detect persons in video frames and draw rectangles around them, which is a core function of the Object Detection API in Azure Cognitive Services Computer Vision.

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Image Analysis (Describe image)

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Optical Character Recognition (OCR)

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Face Detection

Question 15

A logistics company uses drone imagery to monitor a busy container yard. They need to count the exact number of individual shipping containers, even when containers are partially stacked on top of each other or overlapping in the image. Which Azure Computer Vision capability should they choose to achieve the most accurate individual object separation?

Accepted Answer

Instance segmentation. Instance segmentation is the correct choice because it not only detects each individual object in an image but also generates a pixel-level mask for each instance, allowing the model to distinguish between overlapping or stacked objects like shipping containers. This capability provides the most accurate separation of individual containers, even when they partially occlude each other, by assigning unique masks to each instance rather than grouping all containers into a single class.

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Image classification

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Object detection

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Semantic segmentation