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The following graphic shows the results of an unsupervised, machine-learning clustering model: k is the number of clusters, and n is the processing time required to run the model. Which of the following is the best value of k to optimize both accuracy and processing requirements?
Correct Answer: B
# The graph represents a classic "elbow curve," which is often used in clustering (e.g., k-means) to help determine the optimal number of clusters. The point where the curve starts to level off (the "elbow") reflects the best trade-off between model accuracy and processing efficiency. In this graph, the elbow visually occurs around k = 10. Beyond that, the processing time continues to decrease, but the marginal gain in clustering quality (or drop in processing time) diminishes. Why the other options are incorrect: * A: k = 2 underfits the data - too few clusters. * C & D: k = 15 or 20 provides minimal additional benefit in processing but may overcomplicate the model. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 4.2:"The elbow method identifies the optimal number of clusters where the rate of improvement drops significantly." -
