Quick Answer
The answer is to apply a log transformation, specifically the natural log, to the heavily right-skewed 'income' column. This is correct because a log transformation is a concave function that compresses the long tail of a right-skewed distribution, pulling extreme high values closer to the mean and making the overall shape more symmetric and Gaussian-like. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of data preprocessing for ML pipelines, often appearing in scenarios involving AWS Glue ETL jobs where you must choose between log, square root, or Box-Cox transformations. A common trap is confusing log transformation with standardization or min-max scaling, which do not address skewness. Remember the memory tip: "Log for the long tail"—if the tail stretches to the right, the log brings it back into sight.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?
Log transformation (natural log)
Reduces right skewness, makes distribution more symmetric.
Standardization (z-score)
Why wrong: Centers and scales but does not change distribution shape.
Min-max scaling to [0,1]
Why wrong: Only rescales, does not affect skewness.
Equal-width binning
Why wrong: Converts continuous to categorical, discards information.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Log transformation (natural log)
A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.
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.
Log transformation (natural log)
Why this is correct
Reduces right skewness, makes distribution more symmetric.
Related concept
Read the scenario before looking for a memorised answer.
Standardization (z-score)
Why it's wrong here
Centers and scales but does not change distribution shape.
Min-max scaling to [0,1]
Why it's wrong here
Only rescales, does not affect skewness.
Equal-width binning
Why it's wrong here
Converts continuous to categorical, discards information.
Common exam traps
The trap here is that candidates confuse scaling (standardization or min-max) with shape-changing transformations, assuming any normalization makes data Gaussian, when in fact only non-linear transformations like log or Box-Cox address skewness.
Detailed technical explanation
Under the hood, a log transformation is a variance-stabilizing transformation that works well for positive-valued, multiplicative data (e.g., income, population counts) because it converts multiplicative relationships into additive ones. In AWS Glue, using `F.log('income')` in a DynamicFrame or Spark DataFrame applies element-wise natural log, but you must handle zero or negative values (e.g., by adding a small constant like 1) to avoid undefined results. A real-world scenario is preprocessing census income data for linear regression, where log-transformed income often yields residuals that better satisfy normality assumptions.
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: Log transformation (natural log) — A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.
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 24, 2026
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