A company is building an application that needs to generate synthetic data that is based...

Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.

Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.

Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.

Option C: "Residual neural network" is primarily used for improving the performance of deep networks, not for generating synthetic data.

Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.

AWS AI Practitioner References:

GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.