A company needs to create synthetic data that reflects patterns in their existing dataset. Which type of model is appropriate for generating such synthetic data?

A Generative Adversarial Network (GAN) is appropriate for generating synthetic data that reflects patterns in an existing dataset because it consists of two competing neural networks—a generator and a discriminator. The generator learns to create new data instances that resemble the training data, while the discriminator learns to distinguish between real and generated data. Through this adversarial process, the generator becomes highly skilled at producing realistic synthetic data. XGBoost is a gradient boosting algorithm primarily used for classification and regression tasks, not data generation. A Residual Neural Network (ResNet) is a type of convolutional neural network designed to train very deep networks by using skip connections, mainly for image classification or object detection. WaveNet is a deep generative model for raw audio, not general-purpose synthetic data generation.