DA0-002 Data Concepts and Environments Practice Question
A data analyst is working with a dataset that includes a column 'Annual Income' with values ranging from $20,000 to $500,000. The analyst wants to reduce the impact of extreme values on a machine learning model. Which technique should the analyst apply?
⚠ Common exam trap
The trap here is thinking that any scaling method handles outliers; however, only techniques like Winsorizing explicitly limit extreme values.
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
✓
Winsorizing
The correct answer is Winsorizing because it directly caps extreme values at a chosen percentile, reducing their influence on the model. Min-max normalization and z-score standardization do not mitigate outliers, and log transformation only reduces skewness without bounding values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log transformation
Why it's wrong here
Log transformation reduces skewness and compresses the range of large values, but it does not eliminate outliers; it only reduces their relative scale. For income data, it can help, but it does not cap extreme values as directly as Winsorizing. Thus, it is less targeted for reducing outlier impact.
- ✗
Z-score standardization
Why it's wrong here
Z-score standardization transforms data to have a mean of 0 and standard deviation of 1, but it does not bound values and can still be influenced by outliers. Extreme values remain as extreme z-scores, so it does not mitigate their impact. Therefore, it is not appropriate for this scenario.
- ✓
Winsorizing
Why this is correct
Winsorizing replaces extreme values with a specified percentile, such as the 5th and 95th percentiles, effectively capping outliers. This reduces their impact on the model while retaining the data points. It is a robust technique for handling outliers in skewed data like income.
- ✗
Min-max normalization
Why it's wrong here
Min-max normalization scales values to a fixed range, typically 0 to 1, but it is sensitive to outliers because it uses the minimum and maximum values. Extreme values like $500,000 would compress the rest of the data, so it does not reduce outlier impact. Thus, it is not the best technique here.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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