- A
Detecting the same object across multiple images in a photo album
Why wrong: Photo album matching is image search — object tracking maintains persistent IDs across video frames in temporal sequences.
- B
Maintaining the identity of detected objects across consecutive video frames with persistent IDs
Tracking gives each object a consistent ID across frames — enabling trajectory analysis and unique person counting.
- C
Monitoring GPS location of physical objects using IoT sensors
Why wrong: GPS tracking is IoT location monitoring — visual object tracking uses video to follow objects across frames.
- D
Detecting when a tracked object leaves the camera's field of view
Why wrong: Exit detection is one tracking application — object tracking broadly maintains identity across any video frames.
Quick Answer
The answer is that object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, which is fundamentally different from object detection. Object detection identifies and locates objects within a single frame, but it does not remember which object is which once the frame changes. Object tracking builds on detection by linking those same objects frame-to-frame, using motion prediction and appearance matching to preserve a unique label over time. On the Microsoft Azure AI Fundamentals AI-900 exam, this distinction tests your understanding of how Azure Video Indexer or Custom Vision handles video analysis versus static image analysis. A common trap is confusing detection with tracking: detection answers “what and where” in one snapshot, while tracking answers “who and where” across a sequence. For the exam, remember the memory tip: “Detection finds them; tracking binds them.”
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 'object tracking' in computer vision and how does it differ from object detection?
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
Maintaining the identity of detected objects across consecutive video frames with persistent IDs
Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.
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.
- ✗
Detecting the same object across multiple images in a photo album
Why it's wrong here
Photo album matching is image search — object tracking maintains persistent IDs across video frames in temporal sequences.
- ✓
Maintaining the identity of detected objects across consecutive video frames with persistent IDs
Why this is correct
Tracking gives each object a consistent ID across frames — enabling trajectory analysis and unique person counting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Monitoring GPS location of physical objects using IoT sensors
Why it's wrong here
GPS tracking is IoT location monitoring — visual object tracking uses video to follow objects across frames.
- ✗
Detecting when a tracked object leaves the camera's field of view
Why it's wrong here
Exit detection is one tracking application — object tracking broadly maintains identity across any video frames.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection (locating objects in a single frame) with object tracking (maintaining identity across frames), often selecting Option A because they think 'same object across images' implies tracking, but without temporal video context it is just detection or matching.
Detailed technical explanation
How to think about this question
Object tracking often uses algorithms like Kalman filters or optical flow to predict an object's position in the next frame, then associates detections with existing tracks using techniques like the Hungarian algorithm. In Azure Video Indexer, tracking enables features like face re-identification across a video, where a person is assigned a unique ID even if they leave and re-enter the frame. A subtle behavior is that tracking can fail if objects occlude each other, requiring re-identification logic to maintain consistency.
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: Maintaining the identity of detected objects across consecutive video frames with persistent IDs — Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.
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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