AI-900 Describe features of computer vision workloads on Azure Practice Test 5 — 15 Questions

Question 1

A retail chain wants to analyze in-store security camera feeds to count the number of customers entering the store each hour. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it can identify and locate multiple instances of 'person' objects within each video frame, then track and count them over time to determine the number of customers entering per hour. Image classification only labels the entire image with a single category, which cannot provide per-object counts or spatial locations needed for accurate customer counting.

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Image classification

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Optical Character Recognition (OCR)

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Facial recognition

Question 2

A medical research team wants to analyze MRI scans to identify and measure the precise boundaries of tumors. They need to assign each pixel in the image to a class (e.g., tumor, healthy tissue, background). Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation assigns a class label to every pixel in an image, making it the correct choice for precisely delineating tumor boundaries in MRI scans. Azure Computer Vision's semantic segmentation capability outputs a pixel-level mask, enabling the research team to differentiate tumor, healthy tissue, and background at the finest granularity.

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Object Detection

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Image Classification

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Optical Character Recognition

Question 3

A warehouse uses video cameras to monitor a conveyor belt. They need to count the number of boxes passing by each hour to track throughput. Which Azure Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct capability because it can identify and locate multiple boxes within each video frame, allowing the system to count them as they move along the conveyor belt. Unlike image classification, which labels an entire image, object detection provides bounding boxes and counts for each detected object, making it ideal for real-time throughput tracking.

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Optical Character Recognition (OCR)

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Face Detection

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Image Classification

Question 4

A medical research team needs to analyze CT scans to identify and outline the exact boundaries of lung nodules. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, enabling precise delineation of object boundaries. For CT scans, this allows the model to outline the exact shape and contour of lung nodules, which is essential for medical analysis. Image classification and object detection only provide labels or bounding boxes, not pixel-level boundaries.

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Image Classification

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Object Detection

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Optical Character Recognition (OCR)

Question 5

A warehouse uses ceiling-mounted cameras to monitor inventory shelves. The system needs to determine whether each shelf is 'full', 'half full', or 'empty' based on the entire image of the shelf. Which Azure Computer Vision capability should they use?

Accepted Answer

C) Image classification. Image classification (C) is the correct choice because the system needs to assign a single label (full, half full, or empty) to the entire image of a shelf. Azure Computer Vision's image classification analyzes the whole image and outputs a single category or tag, which directly matches the requirement of determining the overall state of the shelf. Object detection would identify and locate multiple objects within the image, not classify the entire scene, and semantic segmentation would assign a label to every pixel, which is overkill for this task.

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A) Optical Character Recognition (OCR)

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B) Object detection

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D) Semantic segmentation

Question 6

A retail chain uses ceiling-mounted cameras to monitor shelf inventory. They need to identify and locate individual products (e.g., a specific brand of cereal) within an image and count how many are present. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 7

A logistics company uses overhead cameras at a shipping dock to read labels on packages. The labels contain text in various fonts, sizes, and orientations, and sometimes the text is partially obscured. Which Azure Computer Vision capability should they use to extract the text from these labels?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.

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Object detection

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Image classification

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Semantic segmentation

Question 8

A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?

Accepted Answer

C. Object detection. Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.

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A. Optical Character Recognition (OCR)

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B. Face detection

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D. Semantic segmentation

Question 9

A photo sharing app wants to automatically generate descriptive captions for uploaded photos to improve accessibility for visually impaired users. Which Azure Computer Vision feature should they use?

Accepted Answer

Describe Image (Image Captioning). Option D is correct because the Describe Image (Image Captioning) feature of Azure Computer Vision generates human-readable captions that describe the content of an image. This directly meets the requirement of automatically generating descriptive captions for uploaded photos to improve accessibility for visually impaired users.

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Optical Character Recognition (OCR)

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Object Detection

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Image Classification

Question 10

A security system uses cameras to detect whether a person is present at a restricted door. Which Azure Computer Vision capability should they use to detect the presence of human faces in the camera images?

Accepted Answer

Face Detection. Face Detection is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face. This capability directly addresses the requirement to detect whether a person is present at a restricted door by identifying faces in camera images, without needing to recognize who the person is.

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Optical Character Recognition (OCR)

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Object Detection

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Image Classification

Question 11

An autonomous drone needs to navigate a forest by identifying individual trees, including their exact shape and boundaries, to avoid colliding with branches. The drone also needs to distinguish between trees and other objects like rocks. Which Azure Computer Vision capability is best suited for this requirement?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.

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Image classification

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Object detection

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Optical character recognition (OCR)

Question 12

A museum wants to automatically generate detailed descriptions of artwork for a mobile app. For each painting, the app should produce a natural-language description that includes the dominant colors, the objects present in the scene, and whether the scene is indoor or outdoor. Which Azure Computer Vision capability is best suited for this task?

Accepted Answer

Image Analysis (Describe Image / Dense Captions). Image Analysis with the Describe Image or Dense Captions API is specifically designed to generate human-readable sentences summarizing the content of an image, including dominant colors, objects, and scene attributes like indoor/outdoor. This capability uses pre-trained deep learning models to produce natural-language descriptions, making it the ideal choice for the museum's requirement of detailed, automated artwork descriptions.

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Optical Character Recognition (OCR)

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Face API

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Object Detection

Question 13

A logistics company needs to automatically read handwritten addresses from package labels using cameras on a conveyor belt. The handwriting varies greatly in style, size, and orientation. Which Azure Computer Vision capability should they use?

Accepted Answer

OCR (Read API). The OCR (Read API) is specifically designed to extract text from images, including handwritten text, and is optimized for varied styles, sizes, and orientations. Unlike standard OCR, the Read API uses deep-learning models to handle unstructured documents and real-world scenarios like package labels on a conveyor belt.

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Image Analysis (describing the image content)

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Face API

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Custom Vision

Question 14

A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?

Accepted Answer

Optical Character Recognition (OCR) - Read API. Option B (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.

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Image Analysis (describe image and detect objects)

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Face API

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Custom Vision (object detection)

Question 15

A manufacturing company uses cameras on an assembly line to inspect products for defects such as scratches, dents, and discoloration. They need to identify the specific type of defect and its location on each product. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of defects (like scratches, dents, or discoloration) but also localizes each defect by drawing bounding boxes around them. This meets the requirement to both classify the specific defect type and report its location on the product.

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Image classification

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Semantic segmentation

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Optical character recognition