AI-900 Describe features of computer vision workloads on Azure Practice Test 12 — 15 Questions

Question 1

What is 'object detection' in computer vision and how does it differ from image classification?

Accepted Answer

Object detection locates each object with a bounding box and class label; classification labels the whole image. Object detection goes beyond image classification by not only identifying the class of objects present but also localizing each one with a bounding box. In contrast, image classification assigns a single label to the entire image, regardless of how many objects are present. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer both capabilities.

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Object detection and image classification produce the same output — both label the entire image

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Image classification processes images faster than object detection because it is simpler

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Object detection only works on images with a single object; classification handles multiple objects

Question 2

What is 'document intelligence' (Azure AI Document Intelligence) and what types of documents can it process?

Accepted Answer

A service that extracts structured data (fields, tables, key-value pairs) from forms and documents. Azure AI Document Intelligence (formerly Form Recognizer) is a service that uses optical character recognition (OCR) and machine learning to extract structured data—such as fields, tables, and key-value pairs—from scanned forms and documents. This enables automated processing of invoices, receipts, business cards, and other structured documents without manual data entry.

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A service that creates documents from structured data in a database

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A document management system for storing and organising files in Azure

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A grammar checking tool that reviews documents for writing quality

Question 3

What is 'background removal' in Azure AI Vision and what is it used for?

Accepted Answer

Automatically separating the foreground subject from the image background. Background removal in Azure AI Vision uses deep learning models to automatically detect and separate the primary foreground subject (e.g., a person, object, or animal) from the rest of the image. The service outputs either a cut-out image with a transparent background or a binary mask, enabling downstream tasks like compositing, product catalog creation, or privacy-focused image processing. This is a core computer vision capability, not related to audio, metadata, or image sharpness.

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Removing background noise from audio in video recordings

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Deleting metadata embedded in image files before uploading to Azure

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Removing blurry or out-of-focus areas from photographs

Question 4

What is 'model confidence score' in Azure Custom Vision predictions?

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A per-prediction certainty measure indicating how sure the model is about a specific classification. In Azure Custom Vision, the model confidence score is a per-prediction value (ranging from 0 to 1) that quantifies the model's certainty that a given input image belongs to a specific class. It is computed during inference based on the probability distribution output by the trained classifier, not during training. This score helps users decide whether to accept or reject a prediction based on a custom threshold.

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The percentage of training images the model correctly labelled during training

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A rating of the training data quality provided by the annotation team

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Microsoft's certification level for how well a Custom Vision model meets enterprise standards

Question 5

What is 'receipt analysis' in Azure AI Document Intelligence and what data does it extract?

Accepted Answer

Extracting merchant name, items, prices, tax, and totals from retail receipt images. Receipt analysis in Azure AI Document Intelligence is a prebuilt model designed to extract key-value pairs and line items from sales receipts. Option B correctly identifies that it extracts merchant name, items, prices, tax, and totals from retail receipt images, which is the primary function of this model.

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Analysing customer satisfaction scores from post-purchase surveys

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Verifying that a receipt matches the purchase record in a financial database

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Detecting fraudulent receipts by comparing them to a known-good receipt database

Question 6

What is 'image captioning' in Azure AI Vision and how is it different from image tagging?

Accepted Answer

Captioning generates a natural language sentence describing the scene; tagging returns individual concept keywords. Image captioning in Azure AI Vision uses a deep learning model to analyze the entire scene and generate a coherent, natural language sentence describing the image content, such as 'a group of people playing soccer in a park.' In contrast, image tagging returns a list of individual keywords or concepts (e.g., 'soccer,' 'grass,' 'people') without forming a complete sentence. This distinction is fundamental to understanding the different outputs of these two Azure AI Vision features.

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Captioning adds user-written descriptions; tagging uses AI to detect objects automatically

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Captioning works on video; tagging works only on still images

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Image tagging is more accurate than captioning because it uses simpler classification

Question 7

What is 'few-shot learning' in the context of Azure AI Custom Vision model training?

Accepted Answer

Training an accurate vision model with very few labelled examples using transfer learning. Few-shot learning in Azure AI Custom Vision refers to training an accurate vision model with very few labeled examples by leveraging transfer learning. This approach uses a pre-trained neural network (e.g., ResNet) as a starting point, allowing the model to learn new visual concepts from as few as 2–5 images per class, significantly reducing the data collection burden.

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Training a model using only a small subset of available compute resources

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A technique for running multiple small training experiments in parallel

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Limiting training to the first few hundred iterations regardless of convergence

Question 8

What is 'multi-modal AI' and how does Azure AI Vision support it?

Accepted Answer

AI that processes and relates multiple data types (text, images, audio) together. Multi-modal AI refers to systems that can process and relate multiple types of data—such as text, images, and audio—simultaneously. Azure AI Vision supports this by providing pre-built models and APIs that extract information from images and video, which can then be combined with text or audio data in a multi-modal pipeline, enabling richer analysis like image captioning or visual question answering.

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AI that processes data in multiple programming languages simultaneously

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Deploying AI models across multiple Azure regions for global availability

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Using multiple AI models in sequence where each model processes a different step

Question 9

What is 'liveness detection' in Azure AI Face service?

Accepted Answer

Verifying that a face presented to a camera is a real live person, not a photo or video replay. Liveness detection in Azure AI Face service is a security feature that distinguishes between a real, live person and a spoofing attempt such as a printed photo, video replay, or a 3D mask. It analyzes subtle cues like eye blinking, skin texture, and depth to ensure the face presented to the camera is physically present and alive. This prevents unauthorized access in identity verification scenarios.

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Detecting whether a celebrity face in a photograph is still alive or deceased

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Detecting human faces in real-time video streaming from security cameras

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Monitoring whether a face recognition model remains accurate after deployment

Question 10

What is 'depth estimation' in computer vision and what are its applications?

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Inferring the distance of objects from the camera to produce a spatial depth map. Depth estimation is a computer vision technique that infers the distance of objects from the camera, producing a spatial depth map where each pixel represents a distance value. This is commonly achieved using stereo vision (two cameras) or monocular depth estimation (single camera with deep learning models). It is a core feature of Azure Computer Vision's spatial analysis capabilities, enabling applications like augmented reality, autonomous navigation, and 3D scene reconstruction.

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Measuring the depth of colour in an image (number of bits per pixel)

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Analysing how deeply a subject is embedded in a complex background scene

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Determining how much detail is captured in a photograph based on lens quality

Question 11

What is 'celebrity recognition' in Azure AI Vision and what are its responsible AI limitations?

Accepted Answer

Recognising well-known public figures in images, with responsible AI access restrictions. Celebrity recognition in Azure AI Vision is a specialized feature that identifies well-known public figures (e.g., actors, politicians, athletes) in images. It is not a general-purpose facial identification service; instead, it relies on a curated dataset of public figures and is subject to responsible AI access restrictions, including limited availability and usage policies to prevent misuse.

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Identifying any person by their face in a photograph using a global identity database

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Automatically tagging images with the names of all people photographed at an event

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A feature available to all Azure customers for identifying any person in any image

Question 12

What is 'scene understanding' in Azure AI Vision?

Accepted Answer

Holistic comprehension of an image's full context, relationships, and scene description. Scene understanding in Azure AI Vision goes beyond simple image classification to provide a holistic comprehension of an image's full context, including objects, their relationships, and a descriptive scene summary. This capability leverages deep learning models to analyze the entire visual content and generate human-readable captions that describe what is happening in the image, such as 'a group of people playing soccer in a park.'

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Classifying images by the type of filming location (indoor, outdoor, urban, rural)

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Breaking an image into individual scenes for video timeline analysis

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Determining the camera settings (ISO, aperture) used to capture a photograph

Question 13

What is 'ID document recognition' in Azure AI Document Intelligence?

Accepted Answer

Extracting structured fields (name, DOB, document number) from passports and identity cards. ID document recognition in Azure AI Document Intelligence is a prebuilt model designed to extract structured fields such as name, date of birth, document number, and expiration date from passports, driver licenses, and identity cards. It uses optical character recognition (OCR) and trained machine learning models to parse the document layout and return key-value pairs, not to verify authenticity or perform identity matching.

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Verifying whether a provided ID document is genuine or a counterfeit

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Assigning employee ID numbers to workers in an HR management system

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Recognising which employees are present using their ID badge photos

Question 14

What is 'video action recognition' in computer vision?

Accepted Answer

Identifying human activities (running, cooking, falling) from temporal patterns across video frames. Video action recognition is a computer vision technique that analyzes sequences of video frames to identify and classify human activities based on temporal patterns and motion cues. Option B correctly describes this as identifying activities like running, cooking, or falling from temporal patterns across frames, which is the core definition used in Azure Video Indexer and other AI services.

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Recognising which video format (MP4, MOV) an uploaded file uses

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Detecting when inappropriate actions are performed in user-generated video content

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Tracking when viewers take actions (like, share, comment) in response to a video

Question 15

What is 'image generation quality' evaluation — how do you measure if a generated image is good?

Accepted Answer

Metrics like FID (image distribution similarity) and CLIP score (prompt adherence), plus human evaluation. Image generation quality is evaluated using a combination of automated metrics and human judgment. FID (Fréchet Inception Distance) measures how similar the distribution of generated images is to real images, while CLIP score assesses how well the image aligns with the given text prompt. Human evaluation is also critical to capture perceptual quality that automated metrics may miss, such as aesthetic appeal and contextual coherence.

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Only image resolution and file size — higher resolution means better quality

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Simply asking the model what score it gives its own output

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Counting the number of objects correctly included vs. missing from the prompt