AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
It's easy for candidates to 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.
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
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.
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's Object Detection project type is the correct Azure AI service for this task. It is trained on images with labeled bounding boxes around the target objects (e.g., hard hats), and during inference it returns the predicted object class, a confidence score, and the x/y coordinates of each bounding box within the image. This provides both the presence and the precise location of a hard hat, which meets the construction safety team's requirement.
- ✗
Computer Vision – Optical Character Recognition (OCR)
Why it's wrong here
Computer Vision's OCR capability only detects and extracts printed or handwritten text from images, such as signs, labels, or documents. It identifies characters and words by using optical character recognition techniques, not by recognizing three-dimensional objects like safety helmets. While OCR can output bounding polygon coordinates for text regions, it cannot classify a hard hat or determine whether a person is wearing proper head protection.
When this WOULD be correct
If the question asked to extract safety compliance numbers or text from hard hat labels in images, OCR would be correct.
- ✗
Face API
Why it's wrong here
The Face API is a specialized Azure AI service for analyzing human faces, including face detection, identification, and attributes like age, emotion, and facial landmarks. It will locate a face region in an image, but it has no object-detection model for equipment such as hard hats, nor does it verify compliance with safety gear. Therefore, even if a worker's face is detected, the Face API cannot answer whether a hard hat is present or where it is located.
When this WOULD be correct
If the question were about identifying workers by their faces (e.g., for access control or attendance tracking) using images, Face API would be the correct service to detect and recognize faces.
- ✗
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.
When this WOULD be correct
If the question asked for a model that determines whether an image contains at least one hard hat (e.g., for a binary yes/no check), Custom Vision Image Classification would be correct, as it outputs a single label per image.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Custom Vision – Object DetectionCorrect answer▾
Why this is correct
Custom Vision's Object Detection project type is the correct Azure AI service for this task. It is trained on images with labeled bounding boxes around the target objects (e.g., hard hats), and during inference it returns the predicted object class, a confidence score, and the x/y coordinates of each bounding box within the image. This provides both the presence and the precise location of a hard hat, which meets the construction safety team's requirement.
✗Computer Vision – Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR extracts text from images, not objects like hard hats. The question requires detecting object locations, not reading text.
★ When this WOULD be the correct answer
If the question asked to extract safety compliance numbers or text from hard hat labels in images, OCR would be correct.
Why candidates choose this
Candidates may confuse 'detecting' with 'reading' or think OCR can identify any visual element, not just text.
✗Face APIWrong answer — click to see why▾
Why this is wrong here
Face API is designed for detecting and analyzing human faces, not for detecting objects like hard hats. The question requires object detection to locate hard hats, which is not a facial feature.
★ When this WOULD be the correct answer
If the question were about identifying workers by their faces (e.g., for access control or attendance tracking) using images, Face API would be the correct service to detect and recognize faces.
Why candidates choose this
Candidates may think Face API can detect any part of a person, including headwear, because hard hats are on the head, but Face API specifically focuses on facial attributes and does not detect objects like hats.
✗Custom Vision – Image ClassificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, not detecting multiple objects or their locations. The question requires identifying the location of each hard hat, which is object detection, not classification.
★ When this WOULD be the correct answer
If the question asked for a model that determines whether an image contains at least one hard hat (e.g., for a binary yes/no check), Custom Vision Image Classification would be correct, as it outputs a single label per image.
Why candidates choose this
Candidates may confuse image classification with object detection, thinking that classifying 'hard hat present' is sufficient, without realizing the need for spatial localization of each hard hat.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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