The answer is Face Detection. This Azure Computer Vision capability is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face, which directly fulfills the requirement to detect whether a person is present at a restricted door without needing to recognize who they are. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Face Detection, which simply finds faces, and Face Recognition, which identifies specific individuals—a common trap where test-takers mistakenly choose the more advanced service. A useful memory tip is to remember that "detection" is about finding a face, while "recognition" is about naming it; for a security system that only needs to know if someone is there, detection is sufficient.
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 security system uses cameras to detect whether a person is present at a restricted door. Which Azure Computer Vision capability should they use to detect the presence of human faces in the camera images?
Why wrong: OCR extracts text from images, not faces.
B
Face Detection
Face Detection is designed to locate human faces in an image, making it the appropriate choice for detecting presence of a person via facial features.
C
Object Detection
Why wrong: Object Detection can detect humans as objects, but Face Detection is more specialized and accurate for detecting faces specifically.
D
Image Classification
Why wrong: Image Classification assigns a label to the entire image (e.g., 'person present'), but it does not locate multiple instances or specific facial regions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Face Detection
Face Detection is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face. This capability directly addresses the requirement to detect whether a person is present at a restricted door by identifying faces in camera images, without needing to recognize who the person is.
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 faces.
✓
Face Detection
Why this is correct
Face Detection is designed to locate human faces in an image, making it the appropriate choice for detecting presence of a person via facial features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Object Detection
Why it's wrong here
Object Detection can detect humans as objects, but Face Detection is more specialized and accurate for detecting faces specifically.
✗
Image Classification
Why it's wrong here
Image Classification assigns a label to the entire image (e.g., 'person present'), but it does not locate multiple instances or specific facial regions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Face Detection with Object Detection, thinking that any object detection model can handle faces equally well, but Azure's Face Detection is a specialized, pre-trained service optimized solely for human faces with additional attributes like face landmarks and attributes not available in generic Object Detection.
Detailed technical explanation
How to think about this question
Azure Face Detection uses deep neural networks trained on millions of face images to output face rectangles, landmarks (e.g., eye corners, nose tip), and optional attributes like head pose or blur. Under the hood, it employs a cascade of convolutional neural networks (CNNs) that first propose candidate face regions and then refine them, achieving high accuracy even with partial occlusion or varied lighting. In a real-world scenario, this capability can be integrated with Azure IoT Edge to run on edge cameras, reducing latency and bandwidth by processing frames locally before sending alerts.
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: Face Detection — Face Detection is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face. This capability directly addresses the requirement to detect whether a person is present at a restricted door by identifying faces in camera images, without needing to recognize who the person is.
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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