The correct action is to apply a log transformation to the 'sqft_living' feature. This is necessary because linear regression assumes that features are approximately normally distributed, and a right-skewed distribution with a long tail violates that assumption, causing the model to be overly sensitive to extreme values and reducing predictive accuracy. By applying a log transformation, you compress the long tail, making the distribution more symmetric and allowing the model to better capture a linear relationship between the feature and the target variable. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering for regression, specifically how skewed features can degrade model performance and that log transformation is a standard remedy. A common trap is to confuse log transformation with scaling methods like standardization or min-max normalization, which do not address skewness. Remember the memory tip: “When the tail is long, log makes it strong.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 machine learning engineer is working on a regression problem to predict house prices. The dataset contains 500,000 rows and 20 features, including 'sqft_living', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', 'yr_built', 'zipcode', and 'lat'. After performing exploratory data analysis, the engineer notices that the 'sqft_living' feature has a right-skewed distribution with a long tail. The 'zipcode' feature is categorical with 70 unique values. The 'lat' feature is continuous. The engineer wants to prepare the data for a linear regression model. Which action should the engineer take to improve model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply a log transformation to the 'sqft_living' feature.
Linear regression assumes that features are approximately normally distributed, and a right-skewed distribution like 'sqft_living' can violate this assumption, leading to poor model performance. Applying a log transformation compresses the long tail, making the distribution more symmetric and helping the model learn a linear relationship between the feature and the target. This is a standard preprocessing step for skewed features in regression tasks.
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.
✗
Remove the 'sqft_living' feature because it violates the normality assumption.
Why it's wrong here
Dropping a potentially important feature is not the best first step.
✓
Apply a log transformation to the 'sqft_living' feature.
Why this is correct
Log transformation reduces right skewness, making the distribution more symmetric.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encode the 'zipcode' feature to capture location effects.
Why it's wrong here
While useful, this does not address the identified skewness in 'sqft_living'.
✗
Apply standard scaling (z-score) to the 'sqft_living' feature.
Why it's wrong here
Scaling does not change the shape of the distribution; skewness remains.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that standard scaling (z-score) can fix skewness, when in reality it only normalizes the mean and variance without altering the shape of the distribution.
Detailed technical explanation
How to think about this question
Log transformation is a variance-stabilizing transformation that maps multiplicative relationships to additive ones, which is particularly useful for features like 'sqft_living' where a 10% increase in square footage might correspond to a constant price increase. Under the hood, the transformation changes the interpretation of the coefficient: a 1% change in the original feature corresponds to a β/100 change in the target. In practice, this is often combined with other preprocessing steps like one-hot encoding categorical features and scaling continuous ones, but for skewed features, log transformation is the primary corrective action.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply a log transformation to the 'sqft_living' feature. — Linear regression assumes that features are approximately normally distributed, and a right-skewed distribution like 'sqft_living' can violate this assumption, leading to poor model performance. Applying a log transformation compresses the long tail, making the distribution more symmetric and helping the model learn a linear relationship between the feature and the target. This is a standard preprocessing step for skewed features in regression tasks.
What should I do if I get this MLS-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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?
easy
A.Apply StandardScaler to the target variable.
B.Apply MinMaxScaler to the target variable.
✓ C.Apply log transformation to the target variable.
D.Apply one-hot encoding to the target variable.
Why C: Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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