Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Is it necessary for labels in a clustering model to be used?

1. Yes

2. No

3. Only for supervised clustering

4. Only for large datasets

The correct answer is: No

In clustering models, it is not necessary to use labels for the data points being analyzed. This is because clustering is an unsupervised learning technique; its primary goal is to group similar data points together based on inherent characteristics without any prior knowledge of labels or categories. Clustering algorithms, such as K-means or hierarchical clustering, identify patterns and structure within the dataset by analyzing relationships among the data points. The data is evaluated based on its features, and the algorithm assigns each data point to a cluster based solely on these characteristics. Since the clusters are formed based on the similarities and differences in the data itself, there are no predefined labels required. While labels can be useful after the clustering has been done—perhaps for validating or interpreting the results—they are not a requirement for performing the clustering process itself. Thus, the correct understanding is that labels are not needed in clustering models.