The answer is facial recognition. While face detection simply locates and identifies the presence of a human face in an image, facial recognition goes a critical step further by matching that detected face against a known database of individuals and returning a confidence score for each match. This directly meets the security company’s need to verify identities against a watchlist, as Azure Computer Vision’s facial recognition capability is specifically built for this identification task. On the AI-900 exam, this distinction is a frequent trap: many learners confuse the two terms, but remember that detection answers “is there a face?” while recognition answers “who is this face?”. The exam tests your ability to choose the right service for identity verification scenarios, often presenting a scenario involving a database of known persons. A simple memory tip: think of “detection” as finding a needle in a haystack, and “recognition” as knowing which needle it is.
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 company needs to identify individuals in a crowd by matching their faces against a database of known persons of interest. The system must detect faces, verify the identities, and provide a confidence score. Which Azure Computer Vision capability should they use?
Facial recognition uses face detection and matching against a known database to identify individuals, exactly as required for this security scenario.
B
Optical character recognition (OCR)
Why wrong: OCR extracts text from images, such as license plates or signs; it cannot recognize or identify human faces.
C
Image classification
Why wrong: Image classification assigns a single label to an entire image (e.g., 'crowd') but does not detect or identify individual people.
D
Object detection
Why wrong: Object detection finds and locates objects like people, cars, etc., but it does not identify specific individuals; it only provides bounding boxes and object classes.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Facial recognition
Azure Computer Vision's facial recognition capability is specifically designed to detect human faces in images, match them against a known database of persons, and return a confidence score for each match. This directly aligns with the security company's requirement to identify individuals in a crowd by verifying their identities against a watchlist.
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.
✓
Facial recognition
Why this is correct
Facial recognition uses face detection and matching against a known database to identify individuals, exactly as required for this security scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical character recognition (OCR)
Why it's wrong here
OCR extracts text from images, such as license plates or signs; it cannot recognize or identify human faces.
✗
Image classification
Why it's wrong here
Image classification assigns a single label to an entire image (e.g., 'crowd') but does not detect or identify individual people.
✗
Object detection
Why it's wrong here
Object detection finds and locates objects like people, cars, etc., but it does not identify specific individuals; it only provides bounding boxes and object classes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'facial recognition' (identity verification against a database) with 'object detection' (locating faces as objects), but only facial recognition provides the identity matching and confidence score required for this scenario.
Detailed technical explanation
How to think about this question
Under the hood, Azure's facial recognition uses deep neural networks to extract unique facial features (e.g., distances between eyes, nose shape) and generates a face template (a vector of numerical values). The system then performs a 1:N search by comparing this template against stored templates in a PersonGroup, returning a confidence score (0.0 to 1.0) for each match. A real-world scenario is law enforcement using live video feeds to identify suspects in real-time, where the confidence threshold must be carefully set to balance false positives and false negatives.
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: Facial recognition — Azure Computer Vision's facial recognition capability is specifically designed to detect human faces in images, match them against a known database of persons, and return a confidence score for each match. This directly aligns with the security company's requirement to identify individuals in a crowd by verifying their identities against a watchlist.
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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