AI0-001 AI Models and Data Engineering Practice Question
A machine learning engineer is preparing a dataset for a model that predicts whether a customer will click on an ad. The dataset contains a feature 'time_since_last_purchase' measured in hours, which has a highly skewed distribution with a long tail. The engineer decides to apply a logarithmic transformation to this feature. Which statement BEST describes the effect of this transformation?
⚠ Common exam trap
Many candidates confuse log transformation with normalization or standardization, and assuming it alone makes features comparable without additional scaling.
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
✓
It reduces the impact of extreme values and makes the distribution more symmetric, which can help linear models.
Applying a logarithmic transformation to a skewed feature like time since last purchase compresses the long tail and reduces skewness. This makes the feature more symmetric, which can improve the performance of models that assume normality or are sensitive to outliers, such as linear models. It does not normalize to a fixed range, bin the feature, or standardize it; those are separate preprocessing steps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It converts the feature into a categorical variable by binning values into logarithmic intervals.
Why it's wrong here
A logarithmic transformation is a mathematical function applied to continuous values, not a binning method. It does not create categories. Binning would discretize the feature, which is different and often loses information. The engineer's action is a continuous transformation to address skew, not to categorize.
- ✗
It normalizes the feature to a 0-1 range, ensuring all features contribute equally to distance calculations.
Why it's wrong here
A logarithmic transformation does not normalize to a 0-1 range; that is typically done with min-max scaling. Log transformation changes the scale but not to a fixed range. While it can help with distance-based models, it does not guarantee equal contribution, and normalization is a separate step. The primary effect here is reducing skew, not bounding values.
- ✓
It reduces the impact of extreme values and makes the distribution more symmetric, which can help linear models.
Why this is correct
A logarithmic transformation compresses the range of large values, reducing skewness and making the distribution closer to normal. This can improve the performance of linear models that assume normally distributed features and are sensitive to outliers. It also stabilizes variance, which is beneficial for models like linear regression or logistic regression.
- ✗
It eliminates the need for feature scaling because the transformed values are already standardized.
Why it's wrong here
Log transformation does not standardize features to zero mean and unit variance; standardization is a separate process. While log-transformed data may have a more symmetric distribution, its scale still depends on the original units. For models sensitive to scale, further standardization might still be needed. Thus, it does not eliminate the need for scaling.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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