- A
An employee wellness programme that tracks steps and exercise using wearables
Why wrong: Wearable health tracking is fitness technology — computer vision safety monitoring uses cameras to detect workplace hazards.
- B
Using computer vision to detect PPE compliance, hazards, restricted zone entry, and safety violations
Safety monitoring AI analyses video for hard hat detection, zone violations, fire detection — reducing workplace accidents.
- C
AI-powered medical diagnosis from health data captured by wearable sensors
Why wrong: Medical diagnosis from wearables is health AI — workplace safety monitoring uses computer vision on physical environment footage.
- D
Monitoring employee screen time and break patterns for ergonomic health compliance
Why wrong: Ergonomic monitoring is occupational health software — computer vision safety monitoring focuses on physical environment hazards.
Health and Safety Monitoring Using Computer Vision
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 'health and safety monitoring' using computer vision and what scenarios does it address?
Quick Answer
The answer is that health and safety monitoring using computer vision is the automated detection of PPE compliance, workplace hazards, restricted zone entry, and safety violations from video or image feeds. This is correct because computer vision models can be trained to recognize specific objects—like hard hats, safety vests, or unauthorized personnel in a marked area—and trigger alerts when those objects are missing or when dangerous conditions appear, enabling proactive enforcement without human oversight. On the AI-900 exam, this scenario tests your understanding of computer vision workloads, often appearing alongside other use cases like object detection or image classification; a common trap is confusing it with simple object counting, but remember that the core goal here is safety compliance and hazard identification, not just inventory tracking. Keep in mind the memory tip: “PPE, Hazards, Zones, Violations” covers the four key detection categories for this workload.
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.
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.
- ✗
An employee wellness programme that tracks steps and exercise using wearables
Why it's wrong here
Wearable health tracking is fitness technology — computer vision safety monitoring uses cameras to detect workplace hazards.
- ✓
Using computer vision to detect PPE compliance, hazards, restricted zone entry, and safety violations
Why this is correct
Safety monitoring AI analyses video for hard hat detection, zone violations, fire detection — reducing workplace accidents.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI-powered medical diagnosis from health data captured by wearable sensors
Why it's wrong here
Medical diagnosis from wearables is health AI — workplace safety monitoring uses computer vision on physical environment footage.
- ✗
Monitoring employee screen time and break patterns for ergonomic health compliance
Why it's wrong here
Ergonomic monitoring is occupational health software — computer vision safety monitoring focuses on physical environment hazards.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision APIs can detect objects (e.g., hard hats, safety vests) and people in video frames, while Azure Video Indexer can analyze motion and spatial relationships to identify zone intrusions. For example, a factory deployment might use a custom object detection model trained on PPE items, triggering alerts when a person enters a hazardous zone without a helmet, using bounding box coordinates and confidence scores from the detection pipeline.
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 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.
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
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