- A
Counting how many different people have used a digital service over a time period
Why wrong: Digital service user analytics use authentication logs — people counting uses computer vision on physical space video feeds.
- B
Using video AI to count people in zones for occupancy, footfall, and queue management
People counting applies spatial analysis to video — enabling real-time occupancy monitoring and footfall analytics.
- C
Identifying and counting employees who have completed mandatory training
Why wrong: Training completion tracking is HR management — people counting is a physical space analytics capability using video.
- D
Counting the number of faces detected in a photo album for tagging purposes
Why wrong: Photo album face counting is a consumer app feature — people counting in spatial analysis monitors real-time occupancy in physical spaces.
Quick Answer
The answer is that people counting in Azure AI Vision spatial analysis uses video AI to count people in zones for occupancy, footfall, and queue management. This is correct because spatial analysis applies computer vision models to detect and track individuals across live or recorded video streams, measuring how many people enter, exit, or remain within defined geographic zones in real time. On the AI-900 exam, this concept tests your understanding of Azure AI Vision’s video-based computer vision capabilities, often appearing in scenario-based questions about retail analytics or workplace safety. A common trap is confusing people counting with facial recognition—remember that spatial analysis focuses on anonymous tracking of bodies, not identities. For the exam, keep in mind that the service processes video frames to output metrics like zone occupancy and dwell time, making it distinct from image-based object detection. Memory tip: think “zones, not faces” to recall that spatial analysis counts anonymous bodies in defined areas for occupancy and flow.
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 'people counting' in Azure AI Vision spatial 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 video AI to count people in zones for occupancy, footfall, and queue management
People counting in Azure AI Vision spatial analysis uses video AI to detect and track individuals within defined zones, enabling accurate measurement of occupancy, footfall, and queue lengths. This is a core computer vision capability that processes live or recorded video streams to count people in real time, supporting retail, workplace, and public safety scenarios.
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.
- ✗
Counting how many different people have used a digital service over a time period
Why it's wrong here
Digital service user analytics use authentication logs — people counting uses computer vision on physical space video feeds.
- ✓
Using video AI to count people in zones for occupancy, footfall, and queue management
Why this is correct
People counting applies spatial analysis to video — enabling real-time occupancy monitoring and footfall analytics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Identifying and counting employees who have completed mandatory training
Why it's wrong here
Training completion tracking is HR management — people counting is a physical space analytics capability using video.
- ✗
Counting the number of faces detected in a photo album for tagging purposes
Why it's wrong here
Photo album face counting is a consumer app feature — people counting in spatial analysis monitors real-time occupancy in physical spaces.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'people counting' with generic face detection or user analytics, but Azure AI Vision spatial analysis specifically requires video input and spatial zone configuration, not static images or digital logs.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision spatial analysis uses deep learning models (e.g., YOLO-based object detection) combined with tracking algorithms (e.g., Kalman filters) to assign unique IDs to individuals as they move through camera frames. It supports zone-based counting (e.g., a rectangle or polygon drawn on the video feed) and line-crossing counting (e.g., a virtual line that increments a counter when crossed). A real-world scenario is a retail store using people counting to measure footfall at entrances and queue wait times at checkout, adjusting staffing dynamically.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of computer vision workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of computer vision workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 video AI to count people in zones for occupancy, footfall, and queue management — People counting in Azure AI Vision spatial analysis uses video AI to detect and track individuals within defined zones, enabling accurate measurement of occupancy, footfall, and queue lengths. This is a core computer vision capability that processes live or recorded video streams to count people in real time, supporting retail, workplace, and public safety scenarios.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What does Azure AI Vision's 'people detection' (spatial analysis) feature track?
medium- A.Identifying the names of specific people in video footage
- ✓ B.Counting, tracking movement, and measuring occupancy of people in defined zones from video
- C.Detecting whether people are wearing masks or safety equipment
- D.Measuring individual people's heights and body dimensions
Why B: Azure AI Vision's spatial analysis (people detection) tracks the movement of people in video feeds, counting individuals and measuring how long they stay in defined zones. It does not identify specific people, detect masks or safety equipment, or measure body dimensions. This feature is designed for occupancy monitoring and flow analysis in physical spaces.
Variation 2. What can Azure AI Vision's spatial analysis feature do?
easy- A.Extract text from documents and images
- ✓ B.Analyze video to detect people's presence and movement in physical spaces
- C.Identify the 3D coordinates of objects in satellite imagery
- D.Generate 3D models from 2D photographs
Why B: Azure AI Vision's spatial analysis feature is designed to analyze video streams from cameras to detect the presence and movement of people in physical spaces. It uses computer vision models to track individuals, count occupancy, and understand movement patterns in real-time, enabling applications like retail analytics or workplace safety.
Variation 3. What is 'spatial analysis' in Azure AI Vision?
medium- A.Analysing the geographic distribution of Azure data centres globally
- ✓ B.Analysing video to understand people's movements and interactions within physical spaces
- C.Mapping pixels in an image to three-dimensional coordinates
- D.Categorising images by their physical dimensions and file size
Why B: Spatial analysis in Azure AI Vision uses video analytics to detect and track people in a physical space, analyzing their movements, positions, and interactions over time. It leverages computer vision models to understand spatial relationships and patterns, such as how people move through a store or queue at a counter.
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.