Quick Answer
The correct data preparation step is to clip negative sales values to zero. This approach directly satisfies the model’s requirement for non-negative input while preserving the temporal structure of the time series, which is critical for forecasting accuracy. By clipping rather than removing or imputing, you treat returns as zero sales events, avoiding the distortion that would come from shifting the entire series or discarding data points. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of domain-aware data cleaning for time series—specifically, how to handle negative values that arise from real-world business logic like returns. A common trap is to assume you must remove those rows or apply interpolation, but that breaks the sequential integrity of the data. Memory tip: think “clip, don’t skip”—clipping keeps the timeline intact while meeting the model’s input constraints.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clip negative sales values to zero
Sets returns to zero, which is appropriate for sales data.
Apply log transformation after adding a constant
Why wrong: Log transform is for positive data; adding constant is arbitrary.
Remove all rows with negative sales values
Why wrong: Loses data on returns, which may be informative.
Impute negative values with the mean
Why wrong: Incorrectly treats negative values as missing; mean may be positive.
Answer choices
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
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.
Detailed technical explanation
In time series forecasting, models like ARIMA or LSTM require non-negative inputs to avoid numerical instability or undefined operations (e.g., log transformations). Clipping is a form of winsorization that caps extreme values at a threshold, preserving the sequence's integrity. A real-world scenario is retail sales forecasting where returns are common; clipping to zero reflects that returns result in no net sale, aligning with business logic.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Clip negative sales values to zero — Option A is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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