Question 1 of 30
A data scientist is tasked with building a deep learning model to classify images of cats and dogs using AWS Deep Learning AMIs. The dataset consists of 10,000 labeled images, with 5,000 images of cats and 5,000 images of dogs. The data scientist decides to use a convolutional neural network (CNN) architecture and wants to optimize the model\'s performance. Which of the following strategies would be the most effective in improving the model\'s accuracy while ensuring efficient use of AWS resources?
Implement data augmentation techniques to increase the diversity of the training dataset.
Increase the number of layers in the CNN without considering the dataset size.
Use a pre-trained model without fine-tuning it on the specific dataset.
Train the model on a smaller subset of the data to save costs.

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