AI-900 Describe features of computer vision workloads on Azure Practice Test 14 — 15 Questions

Question 1

What is 'Azure Custom Vision's training iterations' and why would you train multiple iterations?

Accepted Answer

Versioned training runs — each iteration trains on all current tagged images and can be compared and published. In Azure Custom Vision, a training iteration is a versioned model produced by training on the current set of tagged images. Each iteration captures the model's learned patterns at a specific point in time. Training multiple iterations allows you to compare performance across different hyperparameters, data splits, or image sets, then publish the best-performing iteration to a prediction endpoint for production use.

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Iterations represent attempts to upload images before one succeeds due to network issues

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The number of times the model scans the same image for different object types

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A pricing unit where each API call consumes one iteration from your monthly quota

Question 2

What is 'model export' in Azure Custom Vision and what formats are supported?

Accepted Answer

Exporting trained models as ONNX, TensorFlow, CoreML, or Docker for offline/edge deployment. Model export in Azure Custom Vision allows you to export a trained image classification or object detection model in formats like ONNX, TensorFlow, CoreML, or Docker container images. This enables the model to run offline on edge devices or local servers without requiring a continuous connection to the Azure cloud, which is critical for low-latency or disconnected scenarios.

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Exporting model training logs and metrics to Excel for analysis

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Exporting the training data to another Azure service for fine-tuning

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Exporting a Custom Vision project as a YAML configuration file for source control

Question 3

What is 'smart cropping' in Azure AI Vision and how is it different from simple cropping?

Accepted Answer

AI-guided cropping that keeps the most important content in frame regardless of aspect ratio. Smart cropping in Azure AI Vision uses AI to analyze the image content and intelligently determine the most important region, then crops the image to any specified aspect ratio while keeping that region in frame. This differs from simple cropping, which merely removes pixels from the edges without understanding the image's semantic content. The AI model identifies salient objects, faces, or text to ensure the cropped result remains visually meaningful.

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Cropping images faster using GPU-accelerated image processing

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Automatically cropping out people's faces from images for privacy protection

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Cropping images to remove background noise and irrelevant context

Question 4

What is 'zero-shot object detection' in computer vision?

Accepted Answer

Detecting objects described in text without any training examples of that specific class. Zero-shot object detection refers to a model's ability to detect objects in images based on a textual description of the target class, without having been trained on any labeled examples of that specific class. This is achieved by leveraging a joint embedding space where visual features and text features are aligned, allowing the model to generalize to unseen categories at inference time.

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Object detection that runs with zero latency for real-time applications

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Detection that works on black and white images (zero colour channels)

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An object detection model with zero false positives on the test set

Question 5

What is 'wildlife monitoring' as a computer vision application and what Azure services power it?

Accepted Answer

Using computer vision to identify species, count populations, and track animals from camera trap images. 'wildlife monitoring' in the context of computer vision specifically refers to using AI to automatically analyze camera trap images to identify species, count populations, and track animal movements. Azure services such as Custom Vision (for training species-specific classifiers) and Computer Vision (for image analysis) power this by processing images captured in the field, enabling conservationists to gather data without manual review.

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CCTV monitoring of wildlife parks to ensure visitor safety from animal encounters

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Real-time video monitoring of endangered animal exhibits in zoos for welfare compliance

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AI-powered smart thermostats that monitor and adapt wildlife sanctuary temperatures

Question 6

What is 'Azure AI Vision's landmark detection' and what does it return?

Accepted Answer

Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score. Azure AI Vision's landmark detection is a pre-built computer vision capability that identifies well-known physical landmarks (e.g., Eiffel Tower, Big Ben) in images. It returns the landmark name along with a confidence score indicating the likelihood of the match, enabling applications like automated photo tagging or travel content enrichment.

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Detecting important milestones in a project timeline using AI

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Creating highlighted markers on maps showing user-defined points of interest

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Detecting major architectural features of any building regardless of whether it is famous

Question 7

What does 'confidence score' mean in Azure AI Custom Vision object detection results?

Accepted Answer

The model's certainty about a detection, used to set thresholds balancing false positives vs misses. In Azure AI Custom Vision, the confidence score is a numerical value (0 to 1) that represents the model's certainty that a detected object is correctly identified and localized. This score allows you to set a threshold to filter out low-certainty detections, balancing false positives (detections with low confidence) against misses (true objects that fall below the threshold). It is not a measure of training data composition, test accuracy, or human review.

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The percentage of training images that contained this type of object

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The accuracy of the model measured on the test dataset during training

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A quality rating assigned by human reviewers to confirm the detection is correct

Question 8

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

Accepted Answer

Maintaining the identity of detected objects across consecutive video frames with persistent IDs. Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.

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Detecting the same object across multiple images in a photo album

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Monitoring GPS location of physical objects using IoT sensors

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Detecting when a tracked object leaves the camera's field of view

Question 9

What is 'Azure AI Vision's image moderation' and what content categories does it detect?

Accepted Answer

Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores. Azure AI Vision's image moderation is specifically designed to detect sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is a core feature of the computer vision service that helps platforms comply with content policies by classifying inappropriate visual content rather than modifying images or checking for copyright violations.

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Moderating the resolution and quality of user-uploaded images for platform standards

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Modifying images to blur or remove inappropriate elements automatically

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Detecting copyright violations in user-uploaded images by comparing to known copyrighted works

Question 10

What is 'Azure AI Vision's colour analysis' and what information does it return?

Accepted Answer

Returning dominant colours, accent colour, and B&W detection for image theming and organisation. Azure AI Vision's color analysis extracts color information from images to support theming and organization tasks. It returns the dominant foreground and background colors, an accent color (the most vibrant color suitable for UI theming), and a boolean flag indicating whether the image is black-and-white. This is distinct from image editing or accessibility detection.

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Converting colour images to greyscale for accessibility or artistic purposes

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Adjusting image brightness, saturation, and contrast to optimise visual quality

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Detecting colour-related accessibility issues in user interface designs

Question 11

Drag and drop the steps to process text with Azure Text Analytics (Language service) into the correct order.

Accepted Answer

Step 1: Provision an Azure Text Analytics resource, then Step 2: Retrieve the endpoint and key, then Step 3: Instantiate the Text Analytics client, then Step 4: Submit a document for analysis and process the response.. Using Text Analytics involves setting up a resource, making an API call, and interpreting results.

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Step 2: Retrieve the endpoint and key, then Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 4: Submit a document.

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Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document.

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Step 1: Provision the resource, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document, then Step 3: Instantiate the client.

Question 12

Drag and drop the steps to create a bot with Azure Bot Service into the correct order.

Accepted Answer

Provision Azure Bot Service resource, then develop bot logic, then test the bot locally, then connect to channels.. Creating a bot involves provisioning a resource, developing logic, testing, and connecting channels.

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Develop bot logic, then provision Azure Bot Service resource, then test the bot locally, then connect to channels.

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Provision Azure Bot Service resource, then test the bot locally, then develop bot logic, then connect to channels.

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Connect to channels, then provision Azure Bot Service resource, then develop bot logic, then test the bot locally.

Question 13

Match each Azure AI service to its regional availability constraint.

Accepted Answer

Azure Cognitive Services: Regional availability varies by service and feature.. Regional availability for Azure AI services varies: Cognitive Services have feature-specific limitations, Machine Learning is broadly available but preview features may be restricted, and Bot Service is widely deployed except in some sovereign clouds.

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Azure Cognitive Services: Available in all Azure regions globally without exception.

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Azure Machine Learning: Only available in US East and West Europe.

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Azure Bot Service: Only available in US regions.

Question 14

A city parks department wants to analyze thousands of drone photos to count the number of distinct park benches in each image. The solution must return a bounding box and a label for every bench that is visible. Which Azure AI Vision capability should they use?

Accepted Answer

Object detection in Azure AI Custom Vision. The requirement is to identify multiple instances of a specific object and return both a label and a bounding box for each instance. Object detection in Azure AI Custom Vision is built for exactly this task, allowing the team to train a model on labeled drone imagery and receive per-object locations, which also enables counting.

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Optical character recognition (OCR) in Azure AI Vision

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Image classification in Azure AI Custom Vision

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Face detection in Azure AI Vision

Question 15

A media archive stores millions of scanned newspaper pages. Staff need to search for articles by typed keyword. The pages contain columns of printed text in mixed fonts and some rotated sections. Which Azure AI Vision feature should they use to make the pages searchable?

Accepted Answer

Read API in Azure AI Vision. To make scanned pages searchable, the archive must extract the actual printed words. The Read API in Azure AI Vision performs OCR optimized for documents with mixed fonts, columns, and rotation, returning words and lines that can be indexed for keyword search, which is precisely what the staff need.

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Object detection in Azure AI Custom Vision

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Image tagging in Azure AI Vision

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Face detection in Azure AI Vision