Learn the essential next steps after creating a Custom Vision project for the Azure AI-102 exam. Discover how to train and test your model effectively to ace the certification!
Table of Contents
Question
Your organization, Xerigon Inc., is developing an AI-powered image classification application that will help users automatically categorize large collections of images, such as identifying different types of products or animals in photos.
You create a Custom Vision service to train an object detection model. You have completed the below steps:
- Created Custom Vision resources.
- Created a Custom Vision project.
- Added and tagged images.
- Uploaded the images using the Training API.
What should be your next step in the given scenario?
A. Train and test the model.
B. Publish the model.
C. Connect the COCO file to the dataset.
D. Label the images.
Answer
A. Train and test the model.
Explanation
In the given scenario, your next step would be to train and test the model. During training, the model learns to recognize patterns in the images based on the tags provided. Testing allows you to evaluate the model’s accuracy and performance, ensuring that it can correctly classify new, unseen images. This step is essential to ensure that the model is properly trained and capable of making accurate predictions before it is published for use in production.
Publishing the model is not the next step in the given scenario. Publishing the model is a necessary step when you want to make the trained model available for use in a production environment. However, it would come after the model has been trained and tested to ensure that it performs as expected. Publishing too early, before evaluating the model’s performance, can lead to deploying a model that is inaccurate or not properly optimized.
Labeling the images is not the next step in the given scenario. Labeling or tagging images is an important step in preparing your dataset, as it involves assigning categories or labels to the images. This step is crucial for training the model, but it is done before uploading the images using the Training API. Since the scenario indicates that the images have already been added, tagged, and uploaded, this step has already been completed.
Connecting the COCO file to the dataset is not the next step in the given scenario. A COCO file is a specific format used in object detection that provides information about images, annotations, and categories. Connecting a COCO file may be relevant in certain scenarios involving object detection; however, this is done before training and testing the model.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.