- A
AI that recommends products to online shoppers based on browsing history
Why wrong: Online product recommendation is e-commerce AI — retail intelligence applies computer vision to physical store environments.
- B
Using store video to analyse traffic flow, dwell time, queue length, and planogram compliance
Retail intelligence converts physical store video into actionable analytics — matching the data richness of online shopping analysis.
- C
An AI system that processes retail POS transaction data to forecast sales
Why wrong: POS transaction forecasting is time series ML — retail intelligence uses computer vision on video, not transactional data.
- D
Sentiment analysis of customer reviews from retail websites to improve products
Why wrong: Online review sentiment is NLP — retail intelligence specifically uses computer vision on physical store video feeds.
What Is Retail Intelligence in Azure 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 'retail intelligence' using computer vision and what business value does it provide?
Quick Answer
The correct answer is that retail intelligence in Azure Computer Vision uses in-store video to analyze traffic flow, dwell time, queue length, and planogram compliance. This is correct because the workload relies on computer vision to extract actionable insights from visual data captured by cameras, rather than from transactional or textual records. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure Video Indexer or Custom Vision can process live or recorded video feeds to solve real-world retail problems. A common trap is confusing retail intelligence with sales data analysis or inventory management from databases, but the key is that it always involves visual input from cameras. For a memory tip, think of the four pillars: Traffic, Dwell, Queue, and Planogram—or simply “TDQP” to recall what the cameras are watching.
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 store video to analyse traffic flow, dwell time, queue length, and planogram compliance
Option B is correct because retail intelligence using computer vision involves analyzing video feeds from in-store cameras to extract actionable insights such as customer traffic flow, dwell time at shelves, queue lengths, and planogram compliance. This is a classic computer vision workload on Azure, often implemented using Azure Video Indexer or Custom Vision, which processes visual data rather than transactional or textual data.
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.
- ✗
AI that recommends products to online shoppers based on browsing history
Why it's wrong here
Online product recommendation is e-commerce AI — retail intelligence applies computer vision to physical store environments.
- ✓
Using store video to analyse traffic flow, dwell time, queue length, and planogram compliance
Why this is correct
Retail intelligence converts physical store video into actionable analytics — matching the data richness of online shopping analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
An AI system that processes retail POS transaction data to forecast sales
Why it's wrong here
POS transaction forecasting is time series ML — retail intelligence uses computer vision on video, not transactional data.
- ✗
Sentiment analysis of customer reviews from retail websites to improve products
Why it's wrong here
Online review sentiment is NLP — retail intelligence specifically uses computer vision on physical store video feeds.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse computer vision with other AI workloads like recommendation engines or NLP, assuming any retail AI is 'retail intelligence' without recognizing the specific visual data source.
Detailed technical explanation
How to think about this question
Under the hood, retail intelligence systems use object detection (e.g., YOLO or Azure Custom Vision) to identify products on shelves and people in aisles, combined with tracking algorithms to measure dwell time and queue lengths. A real-world scenario involves detecting planogram compliance by comparing detected shelf arrangements against a reference image, triggering alerts when products are misplaced or out of stock.
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 store video to analyse traffic flow, dwell time, queue length, and planogram compliance — Option B is correct because retail intelligence using computer vision involves analyzing video feeds from in-store cameras to extract actionable insights such as customer traffic flow, dwell time at shelves, queue lengths, and planogram compliance. This is a classic computer vision workload on Azure, often implemented using Azure Video Indexer or Custom Vision, which processes visual data rather than transactional or textual data.
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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