AI0-001 AI Models and Data Engineering Practice Question
A data engineer is preparing a dataset for a machine learning model that predicts customer lifetime value. The dataset contains a 'monthly_spend' column with a highly right-skewed distribution. The team wants to apply a transformation to reduce skewness while preserving the relative order of values. Which transformation is most appropriate?
⚠ Common exam trap
The trap here is assuming that standardization or normalization alone can correct skewness, when in fact they only rescale without altering the distribution's shape.
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
✓
Apply a logarithmic transformation (log(1 + x)) to the 'monthly_spend' column.
A logarithmic transformation is the most appropriate because it compresses large values and reduces right skewness while preserving the relative order of observations. Unlike standardization, it changes the distribution shape; unlike binning or rank transformation, it retains the continuous nature and magnitude relationships, making it well-suited for monetary data that is positively skewed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardize the 'monthly_spend' column by subtracting the mean and dividing by the standard deviation.
Why it's wrong here
Standardization centers the data and scales it to unit variance, but it does not change the shape of the distribution. A right-skewed variable remains right-skewed after z-score normalization. While standardization is useful for many algorithms, it does not address skewness, so it fails to meet the stated goal of reducing skew while preserving order.
- ✗
Replace each value with its rank among all values in the column (rank transformation).
Why it's wrong here
Rank transformation converts values to their ordinal positions, which does preserve order but eliminates the actual magnitude differences. This can reduce skewness but also discards information about the distances between values, which may be important for predicting customer lifetime value. It is not the most appropriate when the goal is to reduce skew while retaining as much information as possible.
- ✗
Apply one-hot encoding to the 'monthly_spend' column by binning into deciles.
Why it's wrong here
One-hot encoding after binning converts a continuous variable into categorical indicators, which discards the ordinal nature and precise values. This increases dimensionality and loses information about relative magnitude, violating the requirement to preserve the relative order of values. It is not a skewness-reduction technique and would harm model performance for predicting a continuous target.
- ✓
Apply a logarithmic transformation (log(1 + x)) to the 'monthly_spend' column.
Why this is correct
A logarithmic transformation compresses the scale of large values while maintaining the order of observations, effectively reducing right skewness. Using log(1+x) handles zero values safely. This is a standard technique for monetary data with positive skew, and it helps linear models and neural networks learn more effectively by making the distribution closer to normal without losing rank information.
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JA
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
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.