- A
Face detection identifies who the person is; face identification counts how many faces are present
Why wrong: This is reversed — detection finds face locations; identification determines who the person is.
- B
Face detection finds face locations; face identification determines who the person is from an enrolled database
Detection = locating faces in an image; identification = matching detected faces to known individuals in an enrolled group.
- C
Face detection works on videos; face identification works on static images only
Why wrong: Both operations can work on images or video frames — the distinction is what they do, not the input type.
- D
They are the same operation with different names
Why wrong: They are fundamentally different operations — detection finds faces, identification matches them to known people.
Quick Answer
The correct answer is that face detection finds face locations, while face identification determines who the person is from an enrolled database. This distinction is rooted in the technical scope of each operation: face detection is a computer vision task that scans an image or video to locate any human faces and returns bounding box coordinates, whereas face identification goes further by matching a detected face against a pre-enrolled database of known individuals to confirm a specific identity. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of Azure Cognitive Services, specifically the Face API, where detection is a prerequisite for identification but does not itself answer "who" the person is. A common trap is confusing identification with verification, which checks if a face matches a single claimed identity rather than searching a database. For a quick memory tip, think of detection as "where is the face?" and identification as "who is that face?"
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 the difference between face detection and face identification?
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
Face detection finds face locations; face identification determines who the person is from an enrolled database
Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines 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.
- ✗
Face detection identifies who the person is; face identification counts how many faces are present
Why it's wrong here
This is reversed — detection finds face locations; identification determines who the person is.
- ✓
Face detection finds face locations; face identification determines who the person is from an enrolled database
Why this is correct
Detection = locating faces in an image; identification = matching detected faces to known individuals in an enrolled group.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Face detection works on videos; face identification works on static images only
Why it's wrong here
Both operations can work on images or video frames — the distinction is what they do, not the input type.
- ✗
They are the same operation with different names
Why it's wrong here
They are fundamentally different operations — detection finds faces, identification matches them to known people.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the terms 'detection' and 'identification' as interchangeable, when in fact detection is a prerequisite for identification and they serve fundamentally different roles in a computer vision pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Azure Face API uses deep neural networks (e.g., ResNet-based models) for detection, outputting face rectangles and optional attributes like landmarks. For identification, the service extracts a unique feature vector (face template) from each detected face and compares it against enrolled templates using cosine similarity, returning the top match if the confidence score exceeds a threshold (default 0.6). A real-world scenario is airport security: detection finds all faces in a camera feed, then identification checks each against a watchlist.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Face detection finds face locations; face identification determines who the person is from an enrolled database — Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines 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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Last reviewed: Jun 11, 2026
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