AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'health and safety monitoring' using computer vision and what scenarios does it address?
⚠ Common exam trap
Test-takers frequently confuse general AI health monitoring (like wearables or ergonomic software) with computer-vision-specific safety monitoring, leading them to pick options that involve non-visual sensor data or administrative tracking rather than image/video 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 detect PPE compliance, hazards, restricted zone entry, and safety violations
Health and safety monitoring using computer vision involves analyzing video feeds or images to automatically detect compliance with personal protective equipment (PPE) requirements, identify workplace hazards, monitor restricted zone entries, and flag safety violations. This is a core computer vision workload on Azure, leveraging services like Azure Video Indexer or Custom Vision to process visual data in real time, enabling proactive safety enforcement without human intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An employee wellness programme that tracks steps and exercise using wearables
Why it's wrong here
Tracking steps and exercise with wearables is a fitness technology use case that ingests sensor data from accelerometers, gyroscopes, and heart-rate monitors, often via Azure IoT Hub, to calculate activity metrics. This approach does not involve computer vision on video footage, and its purpose is to promote personal wellness rather than detect physical hazards in the workplace. Therefore, it is not the computer vision-based safety monitoring described in the scenario.
- ✓
Using computer vision to detect PPE compliance, hazards, restricted zone entry, and safety violations
Why this is correct
This is correct because computer vision workloads in Azure can analyze live or recorded video frames to detect hard hats, safety vests, fire, smoke, and unauthorized entries into restricted zones. Custom Vision and Azure Video Analyzer can be trained on labeled images of personal protective equipment and workplace hazards, then trigger real-time alerts when violations occur. This aligns directly with the AI-900 scenario of safety monitoring to reduce workplace accidents.
- ✗
AI-powered medical diagnosis from health data captured by wearable sensors
Why it's wrong here
AI-powered medical diagnosis from wearable sensors is a health AI use case where models analyze physiological signals like ECG, PPG, or blood oxygen levels to detect conditions such as arrhythmia or sleep apnea. This workload relies on time-series sensor data and clinical ML models, not on analyzing camera images of the physical work environment. Since the scenario specifically asks for computer vision to monitor workplace hazards like PPE compliance and zone entry, this option is incorrect.
- ✗
Monitoring employee screen time and break patterns for ergonomic health compliance
Why it's wrong here
Monitoring employee screen time and break patterns relies on desktop activity logs, application usage data, or keyboard/mouse interaction, not on camera-based visual analysis of the physical environment. Such ergonomic compliance tools are considered occupational health software and typically do not use Azure's computer vision services to detect safety hazards. In contrast, the described safety monitoring uses cameras to identify missing PPE, fires, or restricted zone entry, so this option is incorrect.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Indexer
An indexer is a software component that processes content and builds a searchable index so that users can quickly find documents, data, or files based on keywords or phrases.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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