Quick Answer
The answer is Azure AI Vision Spatial Analysis and Video Indexer. Azure AI Vision’s Spatial Analysis capability is specifically designed for real-time people counting in retail environments by detecting and tracking individuals in camera feeds, while Video Indexer extends this by analyzing recorded video streams to aggregate foot traffic data over time. On the Microsoft Azure AI Engineer Associate AI-102 exam, this pairing tests your understanding of how to combine a real-time vision service with a video analytics pipeline for temporal insights. A common trap is to mistakenly pair Face API or Custom Vision instead, but Spatial Analysis is the only Azure AI Vision feature built for counting people in physical spaces. Remember the memory tip: “Spatial for the spot, Indexer for the span”—Spatial Analysis handles the immediate count, and Video Indexer provides the historical trend.
This AI-102 practice question tests your understanding of implement computer vision solutions. 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.
You are tasked with creating a solution that can identify and count people in a retail store to analyze foot traffic. Which TWO Azure AI services can be used together?
Azure AI Content Safety
Why wrong: Content Safety is not for counting.
Azure AI Document Intelligence
Why wrong: Document Intelligence is for documents.
Azure AI Video Indexer
Video Indexer can detect and count people in videos.
Azure AI Vision Spatial Analysis
Spatial Analysis includes people counting from cameras.
Azure AI Face
Why wrong: Face is for identification, not counting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure AI Video Indexer
Azure AI Vision's people detection counts people in images. Video Indexer can analyze video streams for people counting over time.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure AI Content Safety
Why it's wrong here
Content Safety is not for counting.
Azure AI Document Intelligence
Why it's wrong here
Document Intelligence is for documents.
Azure AI Video Indexer
Why this is correct
Video Indexer can detect and count people in videos.
Related concept
Static NAT maps one inside address to one outside address.
Azure AI Vision Spatial Analysis
Why this is correct
Spatial Analysis includes people counting from cameras.
Related concept
Static NAT maps one inside address to one outside address.
Azure AI Face
Why it's wrong here
Face is for identification, not counting.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
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FAQ
Implement computer vision solutions — This question tests Implement computer vision solutions — Static NAT maps one inside address to one outside address..
The correct answer is: Azure AI Video Indexer — Azure AI Vision's people detection counts people in images. Video Indexer can analyze video streams for people counting over time.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
This AI-102 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-102 exam.
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