Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 70 discussion

B. Measure the model's accuracy against a predefined benchmark dataset: This is the correct strategy for evaluating the performance of a foundation model (FM) in an image classification task. Accuracy is typically evaluated by comparing the model's predictions to the known labels of a benchmark dataset that is representative of the problem domain. This allows you to quantify how well the model is performing.

B. Measure the model's accuracy against a predefined benchmark dataset. Reasoning: Accuracy in Image Classification: The standard way to evaluate the accuracy of a foundation model in image classification tasks is to compare the model's predictions against the ground truth labels in a predefined benchmark dataset. This ensures consistency and reliability in performance evaluation. Benchmark Dataset: A benchmark dataset contains labelled images that serve as a standard for evaluating the performance of image classification models. Examples include ImageNet, CIFAR-10, or MNIST, depending on the task and complexity. Evaluation Metrics: Metrics such as accuracy, precision, recall, and F1 score are typically calculated using the predictions and ground truth labels in the benchmark dataset.

B is good