Answer: Generative adversarial network (GAN)

## Analysis of the Question The question asks: "A company is building an application that needs to generate synthetic data that is based on existing data. Which type of model can the company use to meet this requirement?" ## Evaluation of Options **A: Generative adversarial network (GAN)** - This is the correct answer. GANs are specifically designed architectures for generating synthetic data that mimics real data distributions. They consist of two neural networks (generator and discriminator) that compete in an adversarial process, enabling the generator to produce increasingly realistic synthetic samples based on the patterns learned from existing data. **B: XGBoost** - This is a gradient boosting framework primarily used for supervised learning tasks like classification and regression. While powerful for predictive modeling, XGBoost does not have inherent capabilities for generating new synthetic data samples. It learns patterns to make predictions on existing data but cannot create new data instances. **C: Residual neural network** - ResNets are deep neural network architectures designed to address vanishing gradient problems through skip connections. They excel at tasks like image classification and object detection but are not specifically designed for synthetic data generation. While they can be components in generative models, they are not the optimal choice for this specific requirement. **D: WaveNet** - This is a deep neural network architecture specifically designed for generating raw audio waveforms. While it can generate synthetic audio data, it is specialized for audio applications and not a general-purpose solution for generating synthetic data from arbitrary existing datasets. ## Conclusion Only **Generative adversarial network (GAN)** is specifically designed for the core task of generating synthetic data based on existing data patterns. The other options either serve different primary purposes (XGBoost, ResNet) or are specialized for specific data types (WaveNet). GANs provide the most direct and effective solution for this requirement, as they are fundamentally architected to learn data distributions and generate new, realistic samples that preserve the statistical properties of the original data.

Author: LeetQuiz Editorial Team