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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 chain wants to analyze in-store security camera feeds to count the number of customers entering the store each hour. Which Azure Computer Vision capability should they use?
Why wrong: Image classifies the entire image into one category (e.g., 'indoor', 'crowded'), but does not detect or count multiple objects within the image.
B
Object detection
Object detection locates and identifies multiple objects in an image, such as people. By counting the number of 'person' detections per frame, the system can estimate foot traffic.
C
Optical Character Recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images. It cannot count people or other objects.
D
Facial recognition
Why wrong: Facial recognition identifies or verifies individuals based on facial features. It is not designed for general object counting and raises privacy concerns.
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 'person' objects within each video frame, then track and count them over time to determine the number of customers entering per hour. Image classification only labels the entire image with a single category, which cannot provide per-object counts or spatial locations needed for accurate customer counting.
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.
✗
Image classification
Why it's wrong here
Image classifies the entire image into one category (e.g., 'indoor', 'crowded'), but does not detect or count multiple objects within the image.
✓
Object detection
Why this is correct
Object detection locates and identifies multiple objects in an image, such as people. By counting the number of 'person' detections per frame, the system can estimate foot traffic.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images. It cannot count people or other objects.
✗
Facial recognition
Why it's wrong here
Facial recognition identifies or verifies individuals based on facial features. It is not designed for general object counting and raises privacy concerns.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection with image classification, thinking that classifying an image as 'crowded' or 'empty' is sufficient for counting, when in fact object detection is required to enumerate individual instances.
Detailed technical explanation
How to think about this question
Azure Computer Vision's object detection uses deep learning models (e.g., YOLO or Faster R-CNN) that output bounding boxes and confidence scores for each detected object class. For customer counting, the model is typically trained on the 'person' class from datasets like COCO, and the count is derived by tallying bounding boxes that exceed a confidence threshold (e.g., 0.5) across frames, often combined with tracking algorithms (e.g., SORT or DeepSORT) to avoid double-counting the same individual across consecutive frames.
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 'person' objects within each video frame, then track and count them over time to determine the number of customers entering per hour. Image classification only labels the entire image with a single category, which cannot provide per-object counts or spatial locations needed for accurate customer counting.
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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Question Discussion
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