The answer is Image Analysis with object detection. This capability is correct because Azure Computer Vision’s object detection model can identify multiple instances of a specific class—such as “person”—within a single image, returning bounding box coordinates for each detected individual. This allows the security company to both count the number of people in each camera frame and draw boxes around them programmatically. On the AI-900 exam, this question tests your understanding of the difference between object detection and other Computer Vision features like optical character recognition or image tagging; a common trap is confusing object detection with people counting via a dedicated “people” API, but the exam emphasizes that object detection is the general capability for locating and bounding any object class. To remember this, think: “Object detection draws the box; counting follows from the boxes.”
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 wants to use Azure Computer Vision to monitor a restricted area. They need to count the number of people present in each camera frame and draw bounding boxes around each person. Which Azure Computer Vision capability should they use?
Why wrong: OCR is designed to extract text from images, not to detect or count people.
B
Image Analysis (object detection)
Object detection identifies objects within an image and returns their bounding boxes, making it suitable for counting and locating people.
C
Face detection
Why wrong: Face detection specifically finds human faces, not entire bodies, and may not work if faces are not visible, and it does not provide a count of all people.
D
Image classification
Why wrong: Image classification assigns a label to the entire image but does not provide locations or counts of objects within the image.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Analysis (object detection)
Option B (Image Analysis with object detection) is correct because Azure Computer Vision's object detection capability can identify and locate multiple instances of a specific object class—in this case, people—within an image. It returns bounding box coordinates for each detected person, enabling the security company to count individuals and draw boxes around them in each camera frame.
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 is designed to extract text from images, not to detect or count people.
✓
Image Analysis (object detection)
Why this is correct
Object detection identifies objects within an image and returns their bounding boxes, making it suitable for counting and locating people.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face detection
Why it's wrong here
Face detection specifically finds human faces, not entire bodies, and may not work if faces are not visible, and it does not provide a count of all people.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image but does not provide locations or counts of objects within the image.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing face detection (which only finds faces) with object detection (which finds full people), leading candidates to choose Face detection when the requirement is to count people regardless of face visibility.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision object detection uses a deep neural network (e.g., YOLO or Faster R-CNN variant) trained on the COCO dataset, which includes the 'person' class. The API returns an array of detected objects with confidence scores and bounding box coordinates (left, top, width, height) normalized to the image dimensions. In a real-world scenario, the security system would call the Analyze Image API with the 'visualFeatures' parameter set to 'Objects', then filter results for the 'person' tag and iterate over the bounding boxes to draw rectangles and increment a counter.
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: Image Analysis (object detection) — Option B (Image Analysis with object detection) is correct because Azure Computer Vision's object detection capability can identify and locate multiple instances of a specific object class—in this case, people—within an image. It returns bounding box coordinates for each detected person, enabling the security company to count individuals and draw boxes around them in each camera frame.
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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