- A
Image Analysis (descriptive tags and captions)
Why wrong: Image Analysis can generate tags and captions but does not provide bounding box locations for objects. The department needs to locate objects, not just describe them.
- B
Optical Character Recognition (OCR) API
Why wrong: OCR extracts text from images. It cannot detect or locate non-text objects like cars or pedestrians.
- C
Object Detection (part of Image Analysis 4.0)
Correct. The Object Detection API in Azure Computer Vision can detect and locate common objects in images without any custom training. It returns bounding boxes for objects like cars, people, and bicycles.
- D
Custom Vision object detection
Why wrong: Custom Vision requires uploading labeled images to train a custom model. The department does not have a labeled dataset and wants a prebuilt solution, so Custom Vision is not appropriate.
Quick Answer
The answer is the Object Detection capability within Azure Computer Vision’s Image Analysis 4.0. This prebuilt feature is correct because it can detect and locate common objects like cars, pedestrians, and bicycles in each frame of a live traffic camera feed without requiring any labeled dataset for custom training—it returns bounding box coordinates for every detected object, directly fulfilling the need to “detect and locate.” On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of prebuilt versus custom vision services; a common trap is confusing Object Detection with Optical Character Recognition (OCR) or Image Classification, which only labels the entire image rather than pinpointing object locations. Remember: if the task asks for both detection and location (like finding cars in a traffic scene), think “bounding boxes” and choose Object Detection. A quick memory tip: “Object Detection draws boxes; Classification just labels the whole picture.”
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 city traffic department wants to use Azure Computer Vision to automatically analyze live video feeds from traffic cameras. They need to detect and locate common objects such as cars, pedestrians, and bicycles in each frame. The department does not have a labeled dataset for custom training. Which prebuilt Azure 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 (part of Image Analysis 4.0)
Option C is correct because the Object Detection capability within Image Analysis 4.0 can detect and locate common objects (e.g., cars, pedestrians, bicycles) in images or video frames without requiring any labeled dataset. It provides bounding box coordinates for each detected object, which directly meets the requirement to 'detect and locate' objects in live traffic camera feeds.
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 (descriptive tags and captions)
Why it's wrong here
Image Analysis can generate tags and captions but does not provide bounding box locations for objects. The department needs to locate objects, not just describe them.
- ✗
Optical Character Recognition (OCR) API
Why it's wrong here
OCR extracts text from images. It cannot detect or locate non-text objects like cars or pedestrians.
- ✓
Object Detection (part of Image Analysis 4.0)
Why this is correct
Correct. The Object Detection API in Azure Computer Vision can detect and locate common objects in images without any custom training. It returns bounding boxes for objects like cars, people, and bicycles.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Custom Vision object detection
Why it's wrong here
Custom Vision requires uploading labeled images to train a custom model. The department does not have a labeled dataset and wants a prebuilt solution, so Custom Vision is not appropriate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'descriptive tags' (Option A) with object detection, not realizing that tags only describe the scene without providing spatial location, which is essential for the 'locate' requirement in the question.
Detailed technical explanation
How to think about this question
Under the hood, Azure's Object Detection in Image Analysis 4.0 uses a deep neural network (e.g., YOLO or Faster R-CNN variant) pre-trained on the COCO dataset, which includes 80 common object categories including cars, pedestrians, and bicycles. The API returns bounding box coordinates in normalized pixel values (x, y, width, height) relative to the image dimensions, enabling downstream tracking or counting logic. A subtle behavior is that the model may misclassify partially occluded objects or objects at extreme angles, so real-world deployments often combine object detection with motion-based tracking for robust video analysis.
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 (part of Image Analysis 4.0) — Option C is correct because the Object Detection capability within Image Analysis 4.0 can detect and locate common objects (e.g., cars, pedestrians, bicycles) in images or video frames without requiring any labeled dataset. It provides bounding box coordinates for each detected object, which directly meets the requirement to 'detect and locate' objects in live traffic camera feeds.
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
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.
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