You are developing an application that will recognize faults in components produced on a factory...

Comprehensive Detailed ExplanationYou are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:

Create a project

Before anything else, you must create a Custom Vision project in the Azure AI portal.

This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).

Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.

Upload and tag images

You must provide training data.

Upload a set of images showing both faulty and non-faulty components.

Tag each image correctly so that the model can learn from labeled examples.

Train the classifier model

Once images are tagged, you run training on the dataset to generate the classifier model.

The model can then be published and used for inference via API calls.

Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.

Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.

Why not the other options?

Correct Sequence:

Create a project

Upload and tag images

Train the classifier model

Quickstart: Build an image classification project with the Custom Vision portal

Custom Vision overview

Train and test a Custom Vision model

Microsoft References