AI-900 Describe features of computer vision workloads on Azure Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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

Question 9

A retail company uses ceiling-mounted cameras to monitor shelf stock. They want an automated system that analyzes each camera image to detect if any product is missing from its expected location on the shelf (a product gap). Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.

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Image classification

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Optical Character Recognition (OCR)

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Face detection

Question 10

A nature conservation organization wants to create an app that automatically identifies different species of birds from photos uploaded by birdwatchers. They have thousands of labeled images of bird species. Which Azure service should they use to train a custom model?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model using your own labeled dataset of bird species. Unlike the pre-built Computer Vision Image Analysis service, Custom Vision specializes in fine-grained classification tasks where you need to distinguish between dozens or hundreds of visually similar categories, such as different bird species.

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Azure Computer Vision Image Analysis

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Azure Face API

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Azure Form Recognizer

Question 11

A robotic arm in a factory needs to pick parts from a bin. The system must identify each part and its exact outline to ensure precise grasping. Which Computer Vision capability should be used?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, providing a precise outline of each part. This pixel-level classification is essential for a robotic arm to determine the exact shape and boundaries of parts for accurate grasping, unlike object detection which only provides bounding boxes.

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Object detection

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Image classification

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Optical Character Recognition

Question 12

An autonomous vehicle team needs a system that not only identifies objects like cars and pedestrians but also creates a precise pixel-level mask for each individual object instance, even when objects overlap. Which Azure Computer Vision capability should they use?

Accepted Answer

Instance segmentation. Instance segmentation (Option D) is the correct choice because it combines object detection with semantic segmentation to identify each individual object instance and generate a precise pixel-level mask for it, even when objects overlap. This capability is essential for autonomous vehicles to distinguish between multiple cars or pedestrians that may partially occlude each other, enabling safe navigation.

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Image classification

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Object detection

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Semantic segmentation

Question 13

A museum wants to create an interactive exhibit where visitors can take a photo of a painting. The system should then generate a descriptive caption (e.g., 'A woman with a pearl earring') and classify the painting as either a portrait or landscape. Which Azure Computer Vision capability should they use without needing to train a custom model?

Accepted Answer

Image Analysis. Image Analysis in Azure Computer Vision provides pre-built capabilities for extracting rich information from images, including generating human-readable captions (via the 'describe' operation) and classifying images into categories like 'portrait' or 'landscape' without requiring any custom training. This directly matches the museum's need for both caption generation and orientation classification using a pre-trained model.

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Custom Vision

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Face Detection

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Optical Character Recognition (OCR)

Question 14

A parking management company uses cameras at the entrance and exit of a lot. They need to automatically read the license plate numbers of each car as it enters and exits. Which Azure Computer Vision capability is specifically designed for this task?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images, including license plate numbers. In this scenario, the cameras capture images of cars entering and exiting, and OCR processes those images to read the alphanumeric characters on the license plates. This is the exact use case for OCR, as it can handle varied fonts, angles, and lighting conditions common in parking lot environments.

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Object detection

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Image classification

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Facial recognition

Question 15

A wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?

Accepted Answer

Train a Custom Vision object detection model using the labeled images.. The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.

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Use the pre-built Computer Vision object detection API directly.

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Use the Computer Vision Image Analysis API with the 'dense captioning' feature.

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Train a Custom Vision image classification model with the labeled images.