K-Means Clustering DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data scientist is clustering text documents based on word frequency counts. The features are highly skewed, with many zeros. The scientist considers using Euclidean distance but is concerned about the impact of document length. Which alternative distance metric is most appropriate for this scenario?

   • AChebyshev distance
   • BManhattan distance
   • CCosine similarity
   • DEuclidean distance after standardizing features
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2. 2foundation · easy

   What does a silhouette score measure for a clustering solution?

   • AThe variance of the data within each cluster.
   • BThe total distance between all points and their cluster centroids.
   • CThe number of iterations required for K-means to converge.
   • DHow similar each point is to its own cluster compared to the nearest other cluster.
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4. 3foundation · easy

   Which sequence correctly describes the iterative process of K-means clustering?

   • AInitialize centroids, assign points to nearest centroid, update centroids, repeat until convergence.
   • BUpdate centroids, assign points to nearest centroid, initialize centroids, repeat until convergence.
   • CAssign points to nearest centroid, initialize centroids, update centroids, repeat until convergence.
   • DInitialize centroids, update centroids, assign points to nearest centroid, repeat until convergence.
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5. 4foundation · easy

   In the K-means clustering algorithm, what happens during the assignment step?

   • AThe centroids are recalculated as the mean of all points in each cluster.
   • BEach data point is assigned to the cluster whose centroid is nearest according to the chosen distance metric.
   • CThe number of clusters k is chosen based on the data distribution.
   • DThe initial centroids are placed randomly in the feature space.
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6. 5application · medium

   A marketing team uses K-means to segment customers into three groups. The centroids are: Cluster A: (age=30, spending_score=70), Cluster B: (age=45, spending_score=40), Cluster C: (age=55, spending_score=80). Which cluster represents the most valuable segment for a high-end product campaign?

   • ACluster C, because it has the highest spending score among the centroids.
   • BCluster A, because it has the youngest customers.
   • CAll clusters are equally valuable because they are the same size.
   • DCluster B, because it has the lowest spending score, indicating potential for growth.
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