The answer is object detection. This capability is the correct choice because it identifies and locates multiple instances of people within a video frame by drawing bounding boxes around each person, enabling the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how Azure Computer Vision object detection applies to real-world retail scenarios like customer counting in video, often contrasting it with OCR, image classification, or face detection—traps that lack the spatial localization and multi-instance tracking required. A helpful memory tip: think of object detection as the “where and how many” service—it draws boxes around every person, while classification only says “there is a person” without counting multiple instances.
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.
A retail company wants to automatically analyze in-store video footage to count the number of customers entering and exiting through different doors. Which Azure Computer Vision capability should they use?
Why wrong: OCR extracts text from images, not suitable for detecting or counting people.
B
Image classification
Why wrong: Image classifies the whole image into categories (e.g., 'crowded' vs. 'empty') but does not locate or count individual people.
C
Object detection
Object detection identifies and localizes multiple objects (e.g., persons) in a scene, enabling counting and movement tracking.
D
Face detection
Why wrong: Face detection finds faces, but it may miss people whose faces are not visible (e.g., from behind) and is not designed for general person counting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Object detection
Object detection is the correct capability because it can identify and locate multiple instances of people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.
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.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, not suitable for detecting or counting people.
✗
Image classification
Why it's wrong here
Image classifies the whole image into categories (e.g., 'crowded' vs. 'empty') but does not locate or count individual people.
✓
Object detection
Why this is correct
Object detection identifies and localizes multiple objects (e.g., persons) in a scene, enabling counting and movement tracking.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face detection
Why it's wrong here
Face detection finds faces, but it may miss people whose faces are not visible (e.g., from behind) and is not designed for general person counting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse face detection with person detection, assuming that counting people requires detecting faces, but face detection fails when faces are not visible, whereas object detection with the 'person' class works on full bodies regardless of orientation.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's object detection uses deep learning models like YOLO (You Only Look Once) or Faster R-CNN to output bounding boxes and class labels (e.g., 'person') for each detected object. For counting, the system can be combined with a tracking algorithm (e.g., SORT or Deep SORT) to assign unique IDs to individuals and count them as they cross a virtual line (e.g., a polyline drawn over the door threshold). In a real-world scenario, occlusion (people walking in groups) and varying lighting conditions can challenge accuracy, requiring model retraining with domain-specific data.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Object detection — Object detection is the correct capability because it can identify and locate multiple instances of people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.
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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