- A
CCTV monitoring of wildlife parks to ensure visitor safety from animal encounters
Why wrong: Visitor safety monitoring is a security use case — wildlife monitoring AI studies animal populations and behaviours for conservation.
- B
Using computer vision to identify species, count populations, and track animals from camera trap images
Wildlife AI classifies species and tracks individuals from camera traps — enabling conservation monitoring at scales impossible for humans alone.
- C
Real-time video monitoring of endangered animal exhibits in zoos for welfare compliance
Why wrong: Zoo welfare monitoring is animal care — conservation wildlife monitoring focuses on wild populations in natural habitats.
- D
AI-powered smart thermostats that monitor and adapt wildlife sanctuary temperatures
Why wrong: Temperature control is IoT building management — wildlife monitoring is an ecological application of computer vision AI.
Quick Answer
The correct answer is that wildlife monitoring in Azure Computer Vision uses AI to identify species, count populations, and track animals from camera trap images. This is a specialized computer vision application because it automates the analysis of vast numbers of field images, replacing slow manual review with object detection and classification models that can recognize specific animals, estimate herd sizes, and log movement patterns. On the AI-900 exam, this concept tests your understanding of how Azure Custom Vision and Computer Vision services solve real-world image analysis problems, often appearing in scenario-based questions where you must match the task to the correct service. A common trap is confusing wildlife monitoring with generic image tagging or facial recognition—remember that the key differentiator is the focus on species identification and population counting from camera traps. For a memory tip, think “Wildlife Watch = Custom Vision for species, Computer Vision for analysis,” linking the two services to the core monitoring tasks.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'wildlife monitoring' as a computer vision application and what Azure services power it?
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
Option B is correct because '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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CCTV monitoring of wildlife parks to ensure visitor safety from animal encounters
Why it's wrong here
Visitor safety monitoring is a security use case — wildlife monitoring AI studies animal populations and behaviours for conservation.
- ✓
Using computer vision to identify species, count populations, and track animals from camera trap images
Why this is correct
Wildlife AI classifies species and tracks individuals from camera traps — enabling conservation monitoring at scales impossible for humans alone.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Real-time video monitoring of endangered animal exhibits in zoos for welfare compliance
Why it's wrong here
Zoo welfare monitoring is animal care — conservation wildlife monitoring focuses on wild populations in natural habitats.
- ✗
AI-powered smart thermostats that monitor and adapt wildlife sanctuary temperatures
Why it's wrong here
Temperature control is IoT building management — wildlife monitoring is an ecological application of computer vision AI.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, wildlife monitoring systems often use Azure Custom Vision to train a model on labeled camera trap images (e.g., species bounding boxes) and then deploy it to analyze new images via the Prediction API. A subtle behavior is that these models must handle class imbalance (e.g., rare species) and environmental variation (lighting, occlusion), which can be mitigated by using transfer learning with pre-trained ResNet or EfficientNet backbones. In a real-world scenario, Azure Functions can trigger batch processing of thousands of camera trap images daily, sending results to Cosmos DB for trend analysis.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using computer vision to identify species, count populations, and track animals from camera trap images — Option B is correct because '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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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