- A
Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.
Why wrong: Linear regression is sensitive to multicollinearity.
- B
Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.
Why wrong: Removing all correlated features may discard important information.
- C
Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.
Why wrong: PCA may not be necessary and reduces interpretability.
- D
Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
Log transform addresses skewness; removing one of each pair reduces multicollinearity.
Quick Answer
The correct action is to apply a log transformation to the target variable 'price' and remove either 'sqft_above' or 'sqft_living' along with either 'grade' or 'condition'. This directly addresses the two core data issues: multicollinearity and skewness treatment. Multicollinearity, indicated by high pairwise correlations among features like 'sqft_living' and 'sqft_above' (0.7) or 'grade' and 'condition' (0.8), inflates standard errors in linear regression, making coefficient estimates unstable; removing one feature from each correlated pair resolves this without discarding all predictive power. Simultaneously, right-skewed targets violate linear regression’s normality assumption, so applying a log transformation compresses the tail and normalizes the distribution, improving model fit. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose and treat common regression pitfalls during EDA, with a common trap being to over-remove features or rely on scaling alone. Remember the mnemonic: "Log the skew, drop the pair" — transform the skewed target and drop one feature from each highly correlated pair.
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 data scientist is building a model to predict housing prices using a dataset with 100,000 records and 50 features. The features include 'sqft_living', 'sqft_lot', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', etc. The data scientist uses Amazon SageMaker Data Wrangler for EDA. Upon reviewing the data, the data scientist finds that 'sqft_living' has a correlation of 0.7 with 'sqft_above' (square footage above ground) and 0.6 with 'sqft_basement'. Also, 'grade' (overall grade of the house) is highly correlated with 'condition' (0.8). The target variable 'price' is right-skewed. The data scientist plans to use a linear regression model. Which set of actions should the data scientist take to improve model performance?
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
Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
Option B is correct because log-transforming the target addresses skewness, and removing or combining highly correlated features reduces multicollinearity. Option A is wrong because removing all correlated features may discard predictive power. Option C is wrong because standard scaling does not fix skewness or multicollinearity. Option D is wrong because PCA on all features may lose interpretability and does not target the specific issues.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.
Why it's wrong here
Linear regression is sensitive to multicollinearity.
- ✗
Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.
Why it's wrong here
Removing all correlated features may discard important information.
- ✗
Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.
Why it's wrong here
PCA may not be necessary and reduces interpretability.
- ✓
Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
Why this is correct
Log transform addresses skewness; removing one of each pair reduces multicollinearity.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity. — Option B is correct because log-transforming the target addresses skewness, and removing or combining highly correlated features reduces multicollinearity. Option A is wrong because removing all correlated features may discard predictive power. Option C is wrong because standard scaling does not fix skewness or multicollinearity. Option D is wrong because PCA on all features may lose interpretability and does not target the specific issues.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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.
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