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CompTIA Data+ DA0-002 (V2) Practice Question

While preparing a customer-segmentation dataset for a k-means clustering project, a data analyst notices that the variable annual_spend ranges from 0 to 250 000 dollars, whereas survey_score ranges only from 1 to 5. To keep the larger-scale variable from dominating the Euclidean distance calculation, the analyst wants to re-express each value as the number of standard deviations it lies from that variable's mean. Which data-transformation technique should be applied before running the algorithm?

  • Apply a base-10 logarithm to each feature to compress its scale.

  • Rescale each feature to the interval 0-1 using its minimum and maximum values.

  • Convert every numerical feature to its z-score by subtracting the mean and dividing by the standard deviation.

  • Replace extreme values in each feature with the 5th- and 95th-percentile values.

CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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