The answer is to encode categorical variables using one-hot encoding, apply a log transformation to highly skewed features, and create interaction terms between features. A log transformation normalizes skewed distributions by compressing the range of extreme values, which stabilizes variance and aligns the data with the assumptions of linear models and neural networks—a core technique during feature engineering best practices during EDA. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your ability to identify preprocessing steps that improve model convergence and interpretability, often appearing in scenario-based questions where you must choose between scaling, encoding, or transformation methods. A common trap is selecting binning or normalization alone, but remember that log transforms specifically target skewness, one-hot encoding handles nominal categories without ordinal bias, and interactions capture non-linear relationships missed by individual features. Memory tip: “Log for skew, one-hot for cats, interactions for synergy.”
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.
Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 highly skewed features
Option C is correct because applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.
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 all outliers from the dataset
Why it's wrong here
Removing all outliers may lose important information.
✗
Standardize all features to have zero mean and unit variance
Why it's wrong here
Standardization is not always necessary and depends on the model.
✓
Apply log transformation to highly skewed features
Why this is correct
Log transformation reduces skewness.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Create interaction features between numeric variables
Why this is correct
Interaction features capture relationships between variables.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Encode categorical variables using one-hot encoding
Why this is correct
One-hot encoding is common for categorical variables.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that all preprocessing steps, like outlier removal and standardization, should be performed during EDA, when in fact EDA is for understanding data distributions and relationships, while transformations and scaling are part of data preprocessing that may follow EDA based on insights gained.
Detailed technical explanation
How to think about this question
Log transformation is particularly effective for right-skewed distributions, such as those seen in financial data (e.g., income, transaction amounts) or count data, because it compresses the long tail and makes the data more symmetric. Under the hood, the transformation applies the natural logarithm (or base 10) to each value, which is undefined for zero or negative values, so a constant shift (e.g., log(x+1)) is often used. In real-world scenarios, failing to address skewness can lead to poor model convergence in gradient-based algorithms or biased coefficient estimates in linear regression.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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 log transformation to highly skewed features — Option C is correct because applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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