AWS Certified AI Practitioner Practice Exam

A company has petabytes of unlabeled customer data to classify customers for an advertisement campaign. Which methodology should the company use?

1. Supervised learning

2. Unsupervised learning

3. Reinforcement learning

4. Reinforcement learning from human feedback (RLHF)

The correct answer is: Unsupervised learning

The correct methodology for classifying customers from petabytes of unlabeled data is unsupervised learning. This approach is particularly suitable when the dataset is extensive and lacks labels because it allows the model to identify patterns and relationships within the data without the need for predefined categories or outcomes. Unsupervised learning techniques, such as clustering and dimensionality reduction, are often employed when you want to explore the structure of the data and discover inherent groupings. In this context, the company can utilize these methods to segment customers based on their behaviors or characteristics that may not be immediately apparent or defined. This segmentation can inform targeted advertisement strategies based on the insights gained from the data. Furthermore, unsupervised learning is advantageous in scenarios where labeled data is scarce or expensive to obtain. Since the company is dealing with unlabeled data, unsupervised learning provides a feasible pathway to uncover valuable information about their customer base without the significant resource investment that would be required for labeling petabytes of data.