AI-900: Clustering Model: What Is It and How Does It Work?

Learn about the purpose and applications of clustering models, a type of unsupervised learning, for the Microsoft Azure AI Fundamentals AI-900 certification exam.

Question

What is the purpose of a clustering model?

A. Answers simple two-choice questions
B. Separates similar data points into intuitive groups
C. Makes forecasts by estimating the relationship between values

Answer

B. Separates similar data points into intuitive groups

Explanation

Clustering models have the purpose of separating similar data points into intuitive groups.

The purpose of a clustering model is to separate similar data points into intuitive groups. Clustering is a type of unsupervised learning, which means that the model does not use any labeled data to learn from. Instead, the model tries to find patterns and structures in the data based on some similarity or distance measure. For example, a clustering model can be used to segment customers based on their demographics, preferences, and behavior, or to identify topics in a collection of text documents.

Clustering models are useful for exploratory data analysis, data compression, anomaly detection, and recommendation systems. Some common clustering algorithms are k-means, hierarchical clustering, DBSCAN, and Gaussian mixture models.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.