The correct step before applying a log transformation is to ensure all values in the target variable are positive. This is because the logarithm function is mathematically defined only for positive real numbers; applying it to zero or negative values yields undefined or complex results, which would break the regression model. In the context of the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of fundamental data preparation prerequisites, often appearing as a straightforward trap where candidates overlook handling zeros or negative values. A common scenario involves a skewed target variable with zeros, where the correct approach is to add a constant, such as using log(x + 1), to shift all values into the positive domain before transformation. Remember the memory tip: "Log loves positive—if you see zero or below, shift before you go."
MLA-C01 Data Preparation for Machine Learning Practice Question
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 preparing data for a regression model. The target variable has a skewed distribution. The scientist wants to apply a log transformation to make it closer to normal. Which step should be taken before applying log transformation?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ensure all values are positive
The log transformation is defined only for positive real numbers; applying it to zero or negative values results in undefined or complex outputs. Therefore, before applying a log transformation, you must ensure all values in the target variable are positive, typically by adding a constant (e.g., log(x + 1)) if zeros are present. This step is a fundamental data preparation requirement for log transformations in regression modeling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 data to zero mean and unit variance
Why it's wrong here
Standardization can also result in negative values.
✗
Remove outliers using IQR
Why it's wrong here
Outlier removal is unrelated to the prerequisite of positive values for log.
✓
Ensure all values are positive
Why this is correct
Log is undefined for zero and negative values. If present, add a constant or use other transformations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Center the data by subtracting the mean
Why it's wrong here
Centering can introduce negative values, making log transformation invalid.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the assumption that candidates will confuse data normalization or centering with the domain restriction of the log function, leading them to pick standardization or mean-centering as a preparatory step.
Detailed technical explanation
How to think about this question
Under the hood, the natural logarithm (ln) and base-10 logarithm (log10) are monotonic transformations defined only for x > 0; for x = 0, the limit approaches negative infinity, and for x < 0, the result is complex. In practice, when a target variable contains zeros (common in count data or insurance claims), data scientists often use log(x + 1) or a Yeo-Johnson transformation (which handles negative values) instead of a standard log transform. A real-world scenario is modeling insurance claim amounts, where many claims are zero and the rest are highly skewed; simply applying log without handling zeros would produce errors or infinite values.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Ensure all values are positive — The log transformation is defined only for positive real numbers; applying it to zero or negative values results in undefined or complex outputs. Therefore, before applying a log transformation, you must ensure all values in the target variable are positive, typically by adding a constant (e.g., log(x + 1)) if zeros are present. This step is a fundamental data preparation requirement for log transformations in regression modeling.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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