Question 28
Domain 2: Data ProcessingA data analyst is reviewing several datasets before modeling. In which situation is a log scale transformation most appropriate?
Correct answer: B
Explanation
Use a log scale transformation when data span a wide range of values or are strongly right-skewed, so multiplicative differences become easier to compare. It is most useful when relative change matters more than equal absolute change. — official.txt
Why each option is right or wrong
A. The values are tightly clustered within a narrow range and already symmetric
Log transformation is used when scale differences are large, not when values are already compact and balanced.
B. The values extend across several orders of magnitude and relative differences are important
A log scale transformation is appropriate here because the data cover a very wide range, and the topic specifically targets recognizing scenarios where log scaling should be used. When values differ by factors across orders of magnitude, log scaling makes those relative differences easier to interpret.
C. The categories are nominal labels that need to be converted into indicator variables
Log transformation applies to numeric scale issues, not to encoding nonnumeric category labels.
D. The dataset uses dates and times that need to be standardized into one format
Log transformation addresses numeric magnitude patterns, not formatting of temporal fields.