CompTIA Data+ DA0-002 (V2) Practice Question

Robust scaling uses the median as its center and the interquartile range (IQR) as its scale factor. Because both the median and IQR are resistant to extreme values, the transformation keeps most observations on a comparable scale while preventing a handful of very large lifetime_value records from stretching the range of the whole variable.

Min-max scaling rescales the data between fixed bounds such as 0 and 1, but the presence of even a single extreme value pushes all other observations into a narrow interval, so outliers still dominate. Standardization (Z-score) centers data on the mean and scales by the standard deviation; since both statistics are sensitive to outliers, the resulting values can still be overstretched by extreme cases. A log transform followed by min-max scaling can reduce skew but will still tie the upper bound of the scale to the largest remaining value, offering less protection than an IQR-based approach. Therefore, robust scaling is the most appropriate choice for this scenario.