AWS Certified AI Practitioner Practice Exam

Which method effectively measures the accuracy of a foundation model used in image classification?

1. Calculate the total cost of resources used by the model

2. Measure the model's accuracy against a predefined benchmark dataset

3. Count the number of layers in the neural network

4. Assess the color accuracy of images processed by the model

The correct answer is: Measure the model's accuracy against a predefined benchmark dataset

The most effective method for measuring the accuracy of a foundation model in image classification involves evaluating the model's performance against a predefined benchmark dataset. This practice typically entails using a well-established dataset that contains labeled images in order to analyze how well the model can classify these images correctly. By comparing the model's predictions to the actual labels, you can determine metrics such as accuracy, precision, recall, and F1 score. These metrics provide insightful quantitative data on the model’s performance and its reliability in real-world applications. In contrast, calculating the total cost of resources used by the model does not provide any information about its effectiveness in classifying images. Counting the number of layers in the neural network offers insight into the model's complexity but does not directly correlate with its classification accuracy. Assessing the color accuracy of images processed by the model also does not contribute to a comprehensive measure of the model's classification capabilities as it focuses on a specific attribute rather than its overall performance. Thus, using a benchmark dataset is the most appropriate and effective method for measuring the accuracy of an image classification model.