AI0-001 AI Models and Data Engineering Practice Question
A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?
⚠ Common exam trap
CompTIA often tests the misconception that standardization can fix skewness, but candidates must remember that standardization only rescales the data, not reshape its distribution.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Log transformation
Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality and is not applied to the target variable.
- ✗
Standardization (Z-score)
Why it's wrong here
Standardization centers and scales data but does not reduce skewness.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not for transforming a continuous target.
- ✓
Log transformation
Why this is correct
Log transformation reduces right skewness by compressing large values.
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