Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 17 discussion

Because of the large amounts of unlabeled data and need to identify patterns or groupings within that data, Unsupervised learning is best. Clustering techniques can be used to classify customers into different tiers.

Unsupervised learning is ideal for scenarios where the data is unlabeled, and the goal is to find patterns or groupings within the data. In this case, the company has petabytes of unlabeled customer data and wants to classify customers into tiers. Unsupervised learning techniques, such as clustering (e.g., k-means or hierarchical clustering), can be used to group customers based on similar behaviors, preferences, or attributes without needing predefined labels.

Unsupervised Learning handled the unlabeled datasets

Keyword - Unlabeled Data

B: Unsupervised learning Explanation: Unsupervised learning is used when working with unlabeled data, such as the customer data described in this scenario. This methodology allows the company to identify patterns and group similar customers into clusters or tiers without the need for predefined labels. Techniques like clustering (e.g., K-Means or hierarchical clustering) would help classify customers based on shared characteristics for targeted advertisement campaigns. Why not the other options? A: Supervised learning: Supervised learning requires labeled data, which is not available in this case. Labels would need to be provided for each customer, making this approach unsuitable for the given scenario.

B is the answer..

Unlabeled Data - Unsupervised Learning

B. Unsupervised learning