AI-900 Describe features of computer vision workloads on Azure Practice Test 11 — 15 Questions

Question 1

What is the Azure AI Vision service's 'Image Analysis 4.0' major new capability compared to previous versions?

Accepted Answer

The Florence foundation model enabling detailed captions, dense captioning, background removal, and multimodal embeddings. Image Analysis 4.0 introduces the Florence foundation model, which significantly enhances image understanding capabilities. This model enables detailed captions, dense captioning (generating captions for multiple regions within an image), background removal, and multimodal embeddings that align images and text in a shared vector space. These features go far beyond the classification, object detection, and OCR capabilities of version 3.x.

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Support for processing video files, which was not available in version 3.x

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Support for the first time for color analysis features in images

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The ability to process images larger than 4MB for the first time

Question 2

What is Azure AI Document Intelligence's 'custom extraction model' used for?

Accepted Answer

Training on your labeled documents to extract business-specific fields not covered by prebuilt models. Azure AI Document Intelligence's custom extraction model is correct because it allows you to train a model on your own labeled documents to extract fields that are specific to your business domain and not covered by prebuilt models. This is essential for processing specialized forms like invoices, contracts, or medical records that have unique data fields.

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Automatically generating new document templates from existing forms

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Translating documents into multiple languages simultaneously

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Redacting sensitive information from documents automatically

Question 3

What is the purpose of 'image moderation' using Azure AI Content Safety?

Accepted Answer

Detecting and categorizing harmful content in images (sexual, violent, hate) for automatic content filtering. Azure AI Content Safety's image moderation is designed to detect and categorize harmful content such as sexual, violent, and hate-related material within images. This enables automatic content filtering to ensure compliance with safety policies, which is a core computer vision workload for content moderation.

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Adjusting image brightness and contrast for better display quality

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Verifying that images meet minimum quality standards for AI training

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Compressing images to reduce bandwidth during content delivery

Question 4

What does Azure AI Vision's 'optical character recognition' (OCR) feature do?

Accepted Answer

Extracts printed and handwritten text from images and documents. Azure AI Vision's OCR feature is designed to extract printed and handwritten text from images and documents, converting visual text into machine-readable data. This is correct because OCR uses deep learning models to detect and read text characters from various visual sources, enabling downstream processing like search or analysis.

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Converts text files into images for archival purposes

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Recognises optical fibre cables in data centre photographs

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Corrects spelling errors in text extracted from forms

Question 5

What is 'spatial analysis' in Azure AI Vision?

Accepted Answer

Analysing video to understand people's movements and interactions within physical spaces. Spatial analysis in Azure AI Vision uses video analytics to detect and track people in a physical space, analyzing their movements, positions, and interactions over time. It leverages computer vision models to understand spatial relationships and patterns, such as how people move through a store or queue at a counter.

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Analysing the geographic distribution of Azure data centres globally

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Mapping pixels in an image to three-dimensional coordinates

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Categorising images by their physical dimensions and file size

Question 6

What is 'image segmentation' and how does it differ from object detection?

Accepted Answer

Classifying every pixel in an image to identify precise boundaries — more detailed than bounding-box object detection. Image segmentation classifies every pixel in an image into a category, producing pixel-level masks that outline objects with precise boundaries. This differs from object detection, which only draws bounding boxes around objects and does not distinguish object edges or overlapping instances. Option B correctly captures this higher granularity and accuracy.

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Dividing an image file into smaller files for distributed storage

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Removing the background from an image by detecting edges

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Dividing the training dataset into segments for cross-validation

Question 7

What is 'face detection' vs 'face identification' in Azure AI Vision?

Accepted Answer

Detection locates faces and returns attributes; identification matches faces to a known person database. Face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.

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Face detection and identification are the same feature with different names

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Detection works on live video; identification works only on still images

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Face detection requires a paid tier; identification is available in the free tier

Question 8

What is 'pose estimation' in computer vision and what is it used for?

Accepted Answer

Detecting body keypoint positions (joints) in images to infer posture and movement. Pose estimation is a computer vision technique that detects and localizes keypoints (joints) on a human body in an image or video. These keypoints, such as shoulders, elbows, wrists, hips, and knees, are used to infer the body's posture, orientation, and movement. Option B correctly describes this process of detecting body keypoint positions to infer posture and movement.

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Estimating the correct posture for employees based on ergonomics guidelines

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Determining the camera angle and position used to capture a photograph

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Classifying whether a person is sitting or standing in an image

Question 9

What is 'video indexer' (Azure Video Indexer) and what insights does it extract?

Accepted Answer

A service that extracts transcripts, faces, speakers, topics, and scenes from video content. Azure Video Indexer is a cloud-based service that uses AI to analyze video and audio content. It extracts rich insights such as transcripts (speech-to-text), identified faces, speaker diarization, topics, scenes, and even sentiment, making it a comprehensive media intelligence tool rather than a storage or indexing utility.

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A tool that compresses videos to reduce storage costs in Azure Blob Storage

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A database index that speeds up queries on video metadata tables

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A tool for creating video presentations from a series of images and text

Question 10

What is 'image classification' in Azure AI Custom Vision?

Accepted Answer

Assigning a category label to an entire image based on its dominant visual content. Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.

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Organising image files into folders on Azure Blob Storage by date

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Converting colour images to black and white for accessibility purposes

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Sorting images by their file size and resolution metadata

Question 11

What is 'brand detection' in Azure AI Vision?

Accepted Answer

Identifying well-known brand logos and their locations within images. Brand detection in Azure AI Vision is a specialized feature that uses computer vision models to identify well-known brand logos within images and return their locations as bounding box coordinates. It is part of the Image Analysis API, specifically under the 'brands' visual feature, and does not involve text analysis, resource tagging, or counterfeit detection.

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Detecting counterfeit products by analysing product images

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Analysing brand sentiment from customer review text

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Detecting when Azure resources have been tagged with incorrect brand naming conventions

Question 12

What does the 'image analysis' API in Azure AI Vision return when given an image?

Accepted Answer

Rich metadata including captions, detected objects, tags, colour analysis, and content flags. The Image Analysis API in Azure AI Vision returns rich metadata about the image content, including captions, detected objects, tags, color analysis, and content moderation flags. This is because the API applies pre-trained deep learning models to extract semantic information from the image, not raw pixel data or aesthetic scores.

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The raw pixel data of the image in a compressed format

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A score from 1 to 10 rating the aesthetic quality of the photograph

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A list of similar images found across the web

Question 13

What is 'Azure AI Custom Vision' and how does it differ from Azure AI Vision?

Accepted Answer

Azure AI Vision offers pre-built general models; Custom Vision lets you train models for your specific categories. Azure AI Vision provides pre-trained models for common computer vision tasks like object detection, OCR, and image analysis without requiring custom training data. Azure AI Custom Vision, on the other hand, allows you to upload your own labeled images and train a model to recognize specific categories or objects that are unique to your business scenario. This distinction makes B correct because it highlights the key difference: pre-built general models versus custom-trained models.

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Azure AI Vision is for video; Custom Vision is for still images only

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Custom Vision is more expensive because it uses more advanced AI algorithms

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Azure AI Vision requires GPU compute; Custom Vision runs on CPU only

Question 14

What is 'neural radiance field' (NeRF) technology and how does it relate to Azure AI Vision capabilities?

Accepted Answer

A method for learning 3D scene representations from multiple 2D photographs to enable novel view synthesis. Neural Radiance Fields (NeRF) use a neural network to learn a continuous 5D representation of a scene from a sparse set of 2D photographs, enabling the synthesis of novel views from arbitrary camera angles. This relates to Azure AI Vision capabilities because Azure's Computer Vision services can be integrated with NeRF-based models for advanced 3D reconstruction and volumetric rendering tasks, such as generating immersive 3D assets from 2D images in mixed reality or digital twin scenarios.

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A technique for compressing neural network weights using magnetic fields

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A networking technology that transmits images with zero packet loss

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A type of GPU shader program used for real-time 3D rendering in games

Question 15

What is 'product recognition' in Azure AI Vision for retail scenarios?

Accepted Answer

Identifying retail products and checking shelf placement compliance using computer vision. Product recognition in Azure AI Vision for retail scenarios is specifically designed to identify retail products and check shelf placement compliance using computer vision. It uses object detection and image analysis to recognize products in images or video streams, then compares their placement against a predefined planogram to ensure items are correctly stocked and positioned. This capability helps retailers automate inventory management and optimize shelf layouts.

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Scanning product barcodes to look up inventory information

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Generating product descriptions from images for e-commerce listings

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Detecting counterfeit or damaged products in a manufacturing quality line