Question 757 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is working on a time series forecasting problem. The dataset contains a column 'sales' with occasional negative values due to returns. The model expects non-negative input. Which data preparation step should be taken?
⚠ Common exam trap
AWS often tests the misconception that removing or imputing negative values is safe in time series, but the trap here is that these actions break temporal dependencies and introduce bias, whereas clipping preserves the sequence structure.
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
✓
Clip negative sales values to zero
Clipping negative sales values to zero directly addresses the model's requirement for non-negative input while preserving the data's temporal structure. This approach is appropriate for time series forecasting where returns cause occasional negative values, as it treats returns as zero sales rather than removing or distorting the data points.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Clip negative sales values to zero
Why this is correct
Sets returns to zero, which is appropriate for sales data.
- ✗
Apply log transformation after adding a constant
Why it's wrong here
Log transform is for positive data; adding constant is arbitrary.
- ✗
Remove all rows with negative sales values
Why it's wrong here
Loses data on returns, which may be informative.
- ✗
Impute negative values with the mean
Why it's wrong here
Incorrectly treats negative values as missing; mean may be positive.
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Last reviewed: Jun 30, 2026
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