- A
Custom Vision – Object Detection
Custom Vision object detection can be trained with labeled images that contain bounding boxes around objects of interest, such as hard hats, and then outputs predictions with bounding boxes for new images.
- B
Computer Vision – Optical Character Recognition (OCR)
Why wrong: OCR is used to extract printed or handwritten text from images. It cannot detect objects like hard hats.
- C
Face API
Why wrong: The Face API is designed to detect, identify, and analyze human faces, not objects like hard hats. It may sometimes detect a face but not determine if a hard hat is present.
- D
Custom Vision – Image Classification
Why wrong: Image classification assigns a single label to the entire image (e.g., 'hard hat present' or 'not present'), but it does not provide the location of the hard hat in the image. The requirement is to identify the location, so object detection is necessary.
Quick Answer
The answer is Custom Vision – Object Detection, because this Azure service is specifically built to identify and locate multiple objects within an image by drawing bounding boxes around them, which is exactly what the construction safety team needs to find the position of each hard hat. Unlike image classification, which only labels the entire image, object detection outputs coordinates for each instance of an object, making it ideal for scenarios requiring spatial awareness. On the AI-900 exam, this question tests your ability to distinguish between Computer Vision services: Custom Vision handles custom training with your own labeled images, while pre-built services like the standard Object Detection API are for common objects. A common trap is confusing object detection with image classification—remember, classification answers “what,” but detection answers “where.” For a memory tip, think “Detect to locate, classify to label,” and recall that bounding boxes are the hallmark of object detection tasks.
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 construction safety team wants to automatically detect whether workers on a job site are wearing hard hats by analyzing images from surveillance cameras. They have a large set of labeled images containing workers wearing hard hats and workers without hard hats. The team needs to train a model that can identify the location of each hard hat in an image. Which Azure Computer Vision service 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
Custom Vision – Object Detection
Option A is correct because Custom Vision – Object Detection is specifically designed to identify and locate multiple objects within an image by drawing bounding boxes around them. The construction safety team needs to detect the location of each hard hat, which requires object detection, not just classification. Custom Vision allows training a model with labeled images that include bounding box annotations for objects like hard hats.
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.
- ✓
Custom Vision – Object Detection
Why this is correct
Custom Vision object detection can be trained with labeled images that contain bounding boxes around objects of interest, such as hard hats, and then outputs predictions with bounding boxes for new images.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Computer Vision – Optical Character Recognition (OCR)
Why it's wrong here
OCR is used to extract printed or handwritten text from images. It cannot detect objects like hard hats.
- ✗
Face API
Why it's wrong here
The Face API is designed to detect, identify, and analyze human faces, not objects like hard hats. It may sometimes detect a face but not determine if a hard hat is present.
- ✗
Custom Vision – Image Classification
Why it's wrong here
Image classification assigns a single label to the entire image (e.g., 'hard hat present' or 'not present'), but it does not provide the location of the hard hat in the image. The requirement is to identify the location, so object detection is necessary.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Image Classification with Object Detection, thinking that classifying an image as containing a hard hat is sufficient, but the question explicitly requires identifying the location of each hard hat, which only Object Detection can provide.
Detailed technical explanation
How to think about this question
Under the hood, Custom Vision Object Detection uses a deep learning model based on architectures like Faster R-CNN or YOLO, which output both class probabilities and bounding box coordinates for each detected object. During training, the model learns to map image features to these spatial outputs, enabling precise localization. In a real-world scenario, this allows the safety team to not only count hard hats but also verify that each worker is wearing one, which is critical for compliance and safety audits.
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: Custom Vision – Object Detection — Option A is correct because Custom Vision – Object Detection is specifically designed to identify and locate multiple objects within an image by drawing bounding boxes around them. The construction safety team needs to detect the location of each hard hat, which requires object detection, not just classification. Custom Vision allows training a model with labeled images that include bounding box annotations for objects like hard hats.
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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