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

Question 1

A warehouse uses AI to monitor inventory. They need to detect the presence and location of specific objects (e.g., forklifts, pallets) in real-time video feeds. Which Azure Computer Vision capability should they use?

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

Accepted Answer

Object detection. Object detection is the correct choice because it identifies specific objects (e.g., forklifts, pallets) within an image or video frame and returns bounding box coordinates indicating their location. This capability is designed for real-time spatial awareness, which directly matches the warehouse's need to detect both the presence and position of objects in video feeds.

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Image classification

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OCR (optical character recognition)

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Facial recognition

Question 2

A retail company wants to automatically analyze in-store video footage to count the number of customers entering and exiting through different doors. 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 people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.

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Optical Character Recognition (OCR)

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Image classification

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Face detection

Question 3

An autonomous vehicle system needs to both read the speed limit text on traffic signs and detect the presence and location of pedestrians crossing the road. Which combination of Azure Computer Vision capabilities should be used?

Accepted Answer

Optical Character Recognition (OCR) and Object Detection. The autonomous vehicle system requires two distinct capabilities: reading text from speed limit signs (OCR) and detecting the presence and location of pedestrians (Object Detection). OCR extracts text from images, while Object Detection identifies objects and provides bounding boxes around them, making option C the correct combination.

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Image Classification and OCR

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Semantic Segmentation and OCR

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Face Detection and OCR

Question 4

A logistics company processes packages on an automated conveyor belt. They need to read shipping labels that are often rotated or skewed, and also detect whether a 'FRAGILE' sticker is present on the package. Which combination of Azure Computer Vision capabilities should they use?

Accepted Answer

OCR (Read API) and Object Detection. The scenario requires reading rotated or skewed text from shipping labels (handled by the OCR Read API, which extracts printed and handwritten text from images, even when rotated or skewed) and detecting whether a 'FRAGILE' sticker is present (handled by Object Detection, which identifies and locates specific objects—like stickers—within an image). Option A correctly pairs these two capabilities to meet both requirements.

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Image Classification and OCR (Read API)

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Object Detection and Face Detection

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Image Classification and Face Detection

Question 5

A manufacturing company uses Azure Computer Vision to analyze assembly line images. They need to identify specific product defects (e.g., scratches, dents) and also read serial numbers printed on the products in various fonts. Which combination of Azure Computer Vision features should they use?

Accepted Answer

Custom Vision (object detection) and OCR. The scenario requires two distinct capabilities: identifying specific defect types (scratches, dents) and reading variable-font serial numbers. Custom Vision's object detection model can be trained on labeled defect images to recognize those specific patterns, while Azure's OCR (part of Computer Vision's Read API) extracts printed text regardless of font. Combining these two features directly addresses both requirements.

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Image Analysis (object detection) and OCR

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Face API and OCR

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Image Analysis (tags) and OCR

Question 6

A security company needs to analyze live video feeds from multiple cameras to detect specific objects (e.g., vehicles, people) and also read license plate numbers from vehicles. Which combination of Azure Computer Vision capabilities should they use?

Accepted Answer

Object detection and Optical Character Recognition. The scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.

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Image analysis and face detection

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Semantic segmentation and image captioning

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Spatial analysis and image classification

Question 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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). 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