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

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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.

Question 8

A logistics company scans thousands of packages daily. They need an automated system to read handwritten shipping labels to sort packages correctly. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). The correct answer is B, Optical Character Recognition (OCR), because the scenario requires extracting handwritten text from images of shipping labels to automate sorting. OCR is the specific Azure Computer Vision capability designed to detect and read printed or handwritten text from images, returning machine-readable text that can be used for downstream processing.

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Image Analysis (descriptions and tags)

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Object Detection

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Face API

Question 9

A beverage company uses a camera system to inspect bottles on a conveyor belt. The system must automatically identify which bottles are defective (e.g., cracked or chipped) and which are acceptable, based on the overall appearance of each bottle. The company has thousands of labeled images of bottles (defective and non-defective). Which Azure Computer Vision service should they use to train a custom model?

Accepted Answer

Custom Vision – Image classification. Option B is correct because the scenario requires classifying each bottle image into one of two categories (defective or acceptable) based on overall appearance. Custom Vision – Image classification is designed exactly for this: it trains a model on labeled images to predict a single label per image, making it ideal for binary or multi-class classification tasks like defect detection.

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Custom Vision – Object detection

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Optical Character Recognition (OCR)

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Face API

Question 10

A warehouse deploys cameras to automatically process incoming packages. The system must read the serial numbers printed on each package label to update inventory records. The labels often have varied fonts and sizes, and may be slightly rotated. Which Azure Computer Vision capability should be used to extract the serial numbers?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct Azure Computer Vision capability because it is specifically designed to extract printed or handwritten text from images, including serial numbers with varied fonts, sizes, and rotations. Azure's OCR API (part of Computer Vision) can handle skewed or rotated text by automatically detecting and correcting orientation before recognizing characters, making it ideal for warehouse labels that are not perfectly aligned.

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Object detection

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Image classification

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Facial recognition

Question 11

A real estate agency wants to create a feature on their website that automatically crops uploaded property photos to focus on the house itself, removing excess sky, ground, or other surroundings. Which Azure Computer Vision capability should they use?

Accepted Answer

Smart cropping. Smart cropping is the correct capability because it uses AI to identify the most visually salient region of an image and automatically crops it to focus on the main subject—in this case, the house—while removing irrelevant background like sky or ground. This is distinct from generic cropping as it leverages computer vision to detect the primary object and compositionally frame it.

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OCR (Optical Character Recognition)

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Image captioning

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Object detection

Question 12

A retail company uses overhead cameras to monitor shelf inventory in a store. They want to build a system that automatically detects whether a shelf section is empty or stocked, and specifically identify product categories (e.g., 'soft drinks', 'chips', 'canned goods') and count the number of items in each category. The company has a large set of labeled images showing different shelf states. Which Azure Computer Vision service should they use to build this custom detection and counting solution?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.

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Computer Vision Image Analysis with dense captioning

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Optical Character Recognition (OCR)

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Azure Machine Learning with a pre-trained YOLO model

Question 13

An e-commerce website wants to automatically remove the background from product photos uploaded by sellers so that items appear on a consistent plain background. Which Azure Computer Vision capability should they use?

Accepted Answer

Background Removal. Background Removal is the correct capability because it is specifically designed to isolate the foreground subject from the background in an image, producing a transparent or solid-color background. This directly meets the requirement of automatically removing backgrounds from product photos to create a consistent plain background. Azure's Background Removal API uses deep learning models trained on millions of images to segment the primary object from its surroundings.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 14

A social media platform wants to automatically review user-uploaded images to flag any that contain explicit or suggestive adult content, as well as violent imagery. Which Azure Computer Vision feature should they use?

Accepted Answer

Image Analysis - Moderate content. Option C is correct because the 'Moderate content' feature of Azure Computer Vision is specifically designed to detect adult, suggestive, and violent content in images. It returns a binary flag and confidence scores for categories like adult, racy, and gory, making it the appropriate choice for automatically flagging explicit or violent user-uploaded images.

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Optical Character Recognition (OCR)

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Image Analysis - Tags

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Face Detection

Question 15

A logistics company receives thousands of handwritten shipping forms daily. They need an automated solution to extract the destination address, sender name, and package weight from these forms. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). The correct answer is A, Optical Character Recognition (OCR), because the task requires extracting text (destination address, sender name, package weight) from handwritten shipping forms. Azure's OCR API, part of Computer Vision, is specifically designed to detect and read printed and handwritten text from images, making it the appropriate capability for this document processing scenario.

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Image Analysis

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Face detection

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Custom Vision