Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are creating an Azure AI Custom Vision project. The goal is to identify and locate wooden, plastic, and metal chairs that can appear multiple times in the same photo. You have already collected 120 training images that show different types of chairs in various positions. Which action should you take before training to meet Custom Vision requirements and return bounding-box coordinates when the model is used?
Keep the project as Multilabel Classification and assign all relevant chair tags to each image, then retrain the model.
Create one multiclass classification project for each chair material and ensemble their results programmatically.
Switch the domain to General (Compact) to enable on-device inferencing without adding bounding boxes.
Convert the project to Object Detection, draw a bounding box around every chair, and make sure each tag has at least 15 boxed examples.
Because the requirement is to not only classify chair types but also report the positions of every instance in the picture, you must change the project type to Object Detection. Object-detection projects require that each object in every training image be surrounded by a bounding box and that each tag have at least 15 labeled instances (50 or more are recommended). Multilabel or multiclass classification projects would give only one set of tags per image and would not output the coordinates of each chair, while creating separate projects or choosing a Compact domain does not add localization capability.
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Microsoft Azure AI Engineer Associate AI-102
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