AI-900 Describe features of computer vision workloads on Azure Practice Test 8 — 15 Questions

Question 1

Which Azure AI service enables you to train a custom image classification model with your own labeled images?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision (option B) is the correct service because it is specifically designed to allow users to upload their own labeled images, train a custom image classification model, and then deploy it via a REST API endpoint. Unlike the pre-built Azure AI Vision service, Custom Vision provides the ability to fine-tune a model on domain-specific visual concepts using transfer learning, making it ideal for bespoke classification tasks.

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Azure AI Vision (pre-built)

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Azure Machine Learning

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Azure AI Face

Question 2

Which Azure AI service detects and identifies human faces in images, including attributes like age estimate and emotion?

Accepted Answer

Azure AI Face. Azure AI Face is the correct service because it is specifically designed to detect and identify human faces in images, and it can extract attributes such as age estimates, emotions (e.g., happiness, sadness), and facial landmarks. Unlike general-purpose image analysis, Azure AI Face uses specialized face detection models and returns face rectangles along with optional attribute data.

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Azure AI Vision

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Azure AI Custom Vision

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Azure AI Video Indexer

Question 3

What is Optical Character Recognition (OCR) and which Azure AI service provides it?

Accepted Answer

Technology that extracts text from images; provided by Azure AI Vision. Optical Character Recognition (OCR) is the technology that extracts printed or handwritten text from images, such as scanned documents or photos, and converts it into machine-readable text. This capability is provided by the Azure AI Vision service, specifically through its Read API, which can process both printed and handwritten text from a variety of image formats.

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Speech recognition; provided by Azure AI Speech

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Language translation; provided by Azure AI Translator

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Handwriting analysis for personality assessment; provided by Azure AI Face

Question 4

What is object detection, and how does it differ from image classification?

Accepted Answer

Object detection identifies and locates multiple objects with bounding boxes; image classification labels the whole image. Object detection goes beyond image classification by not only identifying what objects are present in an image but also localizing each object with a bounding box. Image classification assigns a single label to the entire image, whereas object detection can handle multiple objects of different classes simultaneously. This makes object detection suitable for tasks like counting objects or tracking their positions.

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Object detection identifies what is in an image; image classification also identifies where objects are located

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Object detection and image classification are the same task

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Object detection is used only for face recognition

Question 5

Which Azure AI capability can analyze video to identify and track specific people or objects across frames?

Accepted Answer

Azure AI Video Indexer. Azure AI Video Indexer is the correct choice because it is specifically designed to analyze video content, including the ability to detect, track, and identify people or objects across frames using AI-powered computer vision and audio analysis. It provides features like face detection, object tracking, and motion detection over time, making it suitable for this scenario.

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Azure AI Custom Vision

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Azure AI Face

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Azure AI Vision OCR

Question 6

Which Azure AI service can read text from a photo of a street sign taken by a mobile device?

Accepted Answer

Azure AI Vision (Read API / OCR). Azure AI Vision's Read API (part of the Computer Vision service) is designed to extract printed and handwritten text from images, including photos of street signs. It uses optical character recognition (OCR) to detect and digitize text, making it the correct choice for reading text from a mobile device photo.

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Azure AI Speech

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Azure AI Language

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Azure AI Translator

Question 7

What is semantic segmentation in computer vision?

Accepted Answer

Classifying each pixel in an image into a semantic category. Semantic segmentation is a computer vision task that assigns a class label to every single pixel in an image, effectively partitioning the image into regions that correspond to different semantic categories (e.g., road, car, pedestrian). This is distinct from object detection, which only provides bounding boxes around objects, and from image captioning or OCR, which operate at a higher or different level of abstraction.

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Detecting the boundaries of objects using rectangular boxes

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Generating natural language descriptions of images

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Extracting text from images using OCR

Question 8

What can Azure AI Vision's spatial analysis feature do?

Accepted Answer

Analyze video to detect people's presence and movement in physical spaces. Azure AI Vision's spatial analysis feature is designed to analyze video streams from cameras to detect the presence and movement of people in physical spaces. It uses computer vision models to track individuals, count occupancy, and understand movement patterns in real-time, enabling applications like retail analytics or workplace safety.

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Extract text from documents and images

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Identify the 3D coordinates of objects in satellite imagery

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Generate 3D models from 2D photographs

Question 9

Which Azure AI service allows you to analyze medical images for clinical decision support?

Accepted Answer

Azure AI Health Insights and specialized medical imaging AI. Azure AI Health Insights includes specialized medical imaging AI capabilities designed to analyze radiology and other medical images for clinical decision support. This service is built on domain-specific models trained on medical data, unlike general-purpose computer vision services.

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Azure AI Custom Vision with medical training data

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Azure AI Translator for medical documents

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Azure AI Speech for dictation only

Question 10

What does the Azure AI Vision 'Image Analysis' capability return when analyzing an image?

Accepted Answer

Descriptions, objects, tags, and other semantic information about the image content. Azure AI Vision's Image Analysis capability uses pre-trained deep learning models to extract rich semantic information from images, including human-readable descriptions, a list of detected objects with bounding boxes, and a set of relevant tags. This goes far beyond basic metadata, making option B correct because it accurately captures the breadth of semantic outputs the service provides.

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Only the file size and dimensions of the image

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Only a single category label for the entire image

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A 3D point cloud of the scene

Question 11

What is Azure AI Content Safety used for in computer vision scenarios?

Accepted Answer

Detecting harmful or inappropriate content in images for content moderation. Azure AI Content Safety is designed to detect harmful or inappropriate content in images, such as violence, hate speech, self-harm, or sexually explicit material. In computer vision scenarios, it analyzes visual features to classify content into severity levels, enabling automated content moderation. This directly supports safe user-generated content platforms by flagging or blocking prohibited imagery.

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Compressing images to reduce storage costs

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Enhancing image quality and resolution

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Converting images to text descriptions for accessibility

Question 12

What is the Azure AI Vision background removal feature used for?

Accepted Answer

Automatically separating foreground subjects from the background in images. Azure AI Vision background removal is designed to automatically separate foreground subjects from the background in images, producing a mask or a cut-out of the primary object. This feature uses deep learning models to identify and isolate the main subject, enabling further processing like compositing or analysis without the background.

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Blurring the background to create depth of field effects

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Identifying what type of background (indoor/outdoor) is in an image

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Replacing backgrounds in video calls

Question 13

What is facial recognition and what are the key responsible AI considerations for its use?

Accepted Answer

Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse. Facial recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features from images or video. The key responsible AI considerations include addressing accuracy disparities across demographic groups (e.g., higher false positive rates for certain ethnicities), ensuring privacy through data minimization and consent, and preventing misuse such as mass surveillance without oversight. Option B correctly captures these ethical imperatives, which are critical for trustworthy deployment.

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Facial recognition has no ethical concerns and should be deployed universally

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Facial recognition is only used for unlocking smartphones

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Facial recognition is 100% accurate across all demographics

Question 14

What is image classification and how is it different from object detection?

Accepted Answer

Image classification labels the whole image; object detection finds and locates multiple objects within it. Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.

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Image classification is faster; object detection is slower but more accurate

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Image classification works on videos; object detection works on static images only

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They are the same task with different names

Question 15

What does Azure AI Vision's 'dense captioning' feature do?

Accepted Answer

Generates natural language descriptions for multiple regions within a single image. Azure AI Vision's dense captioning feature goes beyond generating a single caption for the entire image. It analyzes the image to identify multiple distinct regions (e.g., a person, a car, a building) and generates a natural language description for each region, along with bounding box coordinates. This is correct because the feature's core purpose is to provide granular, region-level descriptions, not just a single long caption.

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Creates very long detailed captions for entire images

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Extracts text from dense text-heavy images like documents

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Analyzes the density of objects in an image for crowd counting