- A
Face detection and identification are the same feature with different names
Why wrong: These are distinct capabilities — detection finds faces, identification matches them to known individuals.
- B
Detection locates faces and returns attributes; identification matches faces to a known person database
Detection = where are the faces? Identification = who are they? — identification requires enrolment of known faces and additional responsible AI approval.
- C
Detection works on live video; identification works only on still images
Why wrong: Both capabilities can work on video or images — the distinction is what information they return, not the media type.
- D
Face detection requires a paid tier; identification is available in the free tier
Why wrong: Pricing tiers are Azure billing details — the key distinction is functional: detection locates vs. identification names.
Quick Answer
The correct distinction is that face detection locates faces and returns attributes, while face identification matches faces to a known person database. This is correct because Azure AI Vision’s face detection service scans an image for human faces and outputs bounding box coordinates, facial landmarks like eyes and nose, and optional attributes such as age or emotion, but it does not know who the person is. Face identification, part of the Azure Face API, takes that detected face and compares it against a secured PersonGroup to verify or recognize a specific individual. On the AI-900 exam, this tests your understanding of the Azure AI Vision service hierarchy—detection is a prerequisite for identification, and a common trap is confusing identification with the simpler task of finding faces. Remember the memory tip: detection says “there is a face,” identification says “that face is Bob.”
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.
What is 'face detection' vs 'face identification' in Azure AI Vision?
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
Detection locates faces and returns attributes; identification matches faces to a known person database
Option B is correct because face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.
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 and identification are the same feature with different names
Why it's wrong here
These are distinct capabilities — detection finds faces, identification matches them to known individuals.
- ✓
Detection locates faces and returns attributes; identification matches faces to a known person database
Why this is correct
Detection = where are the faces? Identification = who are they? — identification requires enrolment of known faces and additional responsible AI approval.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Detection works on live video; identification works only on still images
Why it's wrong here
Both capabilities can work on video or images — the distinction is what information they return, not the media type.
- ✗
Face detection requires a paid tier; identification is available in the free tier
Why it's wrong here
Pricing tiers are Azure billing details — the key distinction is functional: detection locates vs. identification names.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the terms 'detection' and 'identification' as interchangeable, when Azure explicitly separates them as two distinct API operations with different capabilities and pricing tiers.
Detailed technical explanation
How to think about this question
Under the hood, face detection uses a deep neural network to output a bounding box and 27 facial landmarks per face, while identification computes a unique face template (a vector of up to 512 floating-point numbers) and compares it against enrolled templates in a PersonGroup using cosine similarity. A real-world scenario is airport security: detection finds all faces in a camera feed, then identification matches them against a watchlist database to flag persons of interest. A subtle behavior is that identification requires a confidence threshold (default 0.6) to avoid false positives, and the Face API enforces a maximum of 10,000 persons per PersonGroup.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of computer vision workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of computer vision workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Detection locates faces and returns attributes; identification matches faces to a known person database — Option B is correct because face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
Last reviewed: Jun 11, 2026
This AI-900 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-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.