AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'wildlife monitoring' as a computer vision application and what Azure services power it?
⚠ Common exam trap
A common mix-up: candidates confuse general surveillance or IoT applications with the specific computer vision task of species identification from static images, leading them to pick options that involve real-time video or environmental control rather than image analysis.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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CCTV monitoring of wildlife parks to ensure visitor safety from animal encounters
Why it's wrong here
CCTV for visitor safety is a security-focused workload where the AI monitors human activity and potential animal encounters to protect people, rather than studying wildlife for conservation. The computer vision objective is detecting threats and alerting staff, not classifying species, estimating population sizes, or tracking individual animals in their natural habitat. This is fundamentally different from ecological monitoring because the primary subject and outcome are about human safety, not conservation science.
- ✓
Using computer vision to identify species, count populations, and track animals from camera trap images
Why this is correct
Camera trap images can be processed with computer vision models—such as object detection and image classification—to identify species, count individual animals, and track their movements across large natural areas. This is a canonical conservation-monitoring workload because it automates the analysis of visual data that would otherwise require manual review by ecologists. In Azure, Custom Vision or the Computer Vision API can classify wildlife and draw bounding boxes around animals in each captured image, making this the correct answer.
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Real-time video monitoring of endangered animal exhibits in zoos for welfare compliance
Why it's wrong here
Real-time video in zoo exhibits may use computer vision to assess animal welfare, but this is a captive-animal-care compliance scenario rather than an ecological conservation workload. The focus is on individual animals in managed environments and on meeting animal-welfare standards, not on identifying species, counting wild populations, or tracking animals in natural habitats. Therefore, it does not match the conservation-monitoring purpose described in the question.
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AI-powered smart thermostats that monitor and adapt wildlife sanctuary temperatures
Why it's wrong here
Smart thermostats rely on IoT sensors and temperature-control algorithms, not on computer vision analysis of images or video. Although they might monitor and adjust the climate inside a wildlife sanctuary building, they cannot identify species, count populations, or track animals from visual data. This is an environmental-control and building-automation use case, completely different from a computer vision conservation-monitoring application.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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