Struggling with Azure Custom Vision image labeling for the AI-102 exam? Discover step-by-step solutions, common mistakes to avoid, and expert tips to ace image classification tasks on the Microsoft Azure AI Engineer Associate 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.
To train the AI model, you are creating a Custom Vision project. You have created the Blob, uploaded images to blob storage, and created your dataset.
What should you do next?
A. Create a new Azure AI Services resource.
B. Label your images.
C. Train the data model on the dataset.
D. Connect the COCO file to the dataset.
Answer
B. Label your images.
Explanation
In the given scenario, your next step would be to label your images. Labeling is annotating your images with tags or categories corresponding to the objects or features you want your AI model to recognize. Proper labeling ensures that the model learns to associate specific patterns in the images with the correct categories during training. Without labeling, the model will not know how to categorize the images, leading to inaccurate or ineffective results.
Connecting the Common Objects in Context (COCO) file to the dataset is not the next step in the given scenario. You would connect the COCO file for the labeled images to the dataset after labeling the images. A COCO file is a JSON file structured to include:
- Images: Specifies the image’s location in blob 0storage, along with details such as its name, dimensions (width and height), and a unique identifier (ID).
- Annotations: Contains classification information, identifying the category to which the image belongs, and, for object detection, includes details such as the area and bounding box coordinates.
- Categories: Lists the unique identifiers (IDs) corresponding to each named label class.
Training the data model on the dataset is not the next step in the given scenario. You would train the data model after labeling the images and connecting the COCO2.5.2. file to the dataset. Attempting to train the model without labeled data will result in a model that cannot learn the correct associations between image features and categories. Once the images have been labeled, you can proceed with training the model to recognize patterns and categorize images accurately.
Creating a new Azure AI Services resource is not the next step in the given scenario. Creating an Azure AI Services resource is an important step when starting your project. However, since the scenario specifies that you have already created your dataset, this step should have been completed earlier.
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.