- A
Image Analysis (Describe image)
Why wrong: Image Analysis generates a human-readable description of the image content and returns tags, but it does not provide bounding box coordinates for detected objects.
- B
Object Detection
Object Detection is designed to locate and classify multiple objects in an image, returning bounding box coordinates for each detected object. It can detect people among other object classes.
- C
Optical Character Recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images. It cannot detect people or draw bounding boxes around non-text objects.
- D
Face Detection
Why wrong: Face Detection identifies human faces and returns bounding boxes around each face, not the full body. For detecting a person (whole body), object detection is more suitable.
Quick Answer
The answer is Object Detection, the Azure Computer Vision capability that detects persons and draws bounding boxes around them. This is correct because Object Detection goes beyond simply classifying an image’s content—it identifies specific objects (like people) and returns their precise pixel coordinates, allowing a rectangle to be drawn around each detected instance in a video frame. On the AI-900 exam, this question tests your understanding of the difference between Object Detection and other Computer Vision features like Image Classification (which only labels the whole image) or Semantic Segmentation (which labels pixels but doesn’t draw boxes). A common trap is confusing Object Detection with the People Counting feature, but remember: bounding boxes are the hallmark of detection. For a memory tip, think “Detect and Box”—Object Detection finds the person and puts a box around them, exactly as the security company needs.
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 monitor a restricted area using camera feeds. The system must detect if a person is present in each video frame and draw a rectangle around each detected person. Which Azure Cognitive Services Computer Vision capability should they use?
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
Object Detection
Object Detection is the correct capability because it identifies and locates objects (including people) within an image by drawing bounding boxes around each detected instance. This directly matches the requirement to detect persons in video frames and draw rectangles around them, which is a core function of the Object Detection API in Azure Cognitive Services Computer Vision.
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.
- ✗
Image Analysis (Describe image)
Why it's wrong here
Image Analysis generates a human-readable description of the image content and returns tags, but it does not provide bounding box coordinates for detected objects.
- ✓
Object Detection
Why this is correct
Object Detection is designed to locate and classify multiple objects in an image, returning bounding box coordinates for each detected object. It can detect people among other object classes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images. It cannot detect people or draw bounding boxes around non-text objects.
- ✗
Face Detection
Why it's wrong here
Face Detection identifies human faces and returns bounding boxes around each face, not the full body. For detecting a person (whole body), object detection is more suitable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Face Detection (which only finds faces) with Object Detection (which finds full persons and other objects), leading them to choose D when the requirement is to detect entire people, not just their faces.
Detailed technical explanation
How to think about this question
The Object Detection API in Azure Computer Vision uses a deep learning model (e.g., YOLO or Faster R-CNN) trained on the COCO dataset, which includes the 'person' class. It returns bounding box coordinates in normalized pixels (left, top, width, height) along with confidence scores, enabling precise localization. In a real-world security scenario, this allows the system to track multiple people across frames, even when faces are occluded, whereas Face Detection would fail if a person's face is not visible.
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
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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: Object Detection — Object Detection is the correct capability because it identifies and locates objects (including people) within an image by drawing bounding boxes around each detected instance. This directly matches the requirement to detect persons in video frames and draw rectangles around them, which is a core function of the Object Detection API in Azure Cognitive Services Computer Vision.
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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