AWS Certified AI Practitioner Practice Exam

Data augmentation for imbalanced classes is an effective technique for addressing issues related to biased input data, especially when a model is being trained to generate images of humans in various professions. This technique involves increasing the diversity of the training dataset without actually collecting new data.

For example, if certain professions are underrepresented in the training data, data augmentation can help by artificially expanding the dataset. This can be done through transformations like rotation, scaling, flipping, or even more complex methods like generating synthetic images using GANs (Generative Adversarial Networks). By ensuring that the model is exposed to a more balanced representation of various professions, it can learn more generalized features rather than associating specific traits or appearances with only a few professions.

In contrast to this approach, model monitoring for class distribution primarily focuses on observing how many examples of each class the model sees during inference or validation, which is useful but doesn't directly address the underlying issue of biased training data. Retrieval Augmented Generation (RAG) is more suited for natural language tasks, and watermark detection is not relevant in the context of mitigating bias in input data.