This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 customer churn. The dataset includes both numerical features (e.g., account age, usage minutes) and categorical features (e.g., region, plan type). The data scientist wants to use a linear classifier. Which feature engineering step is required before training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One-hot encode categorical features
Linear classifiers (e.g., logistic regression, linear SVM) require numerical input and cannot directly process categorical text labels. One-hot encoding converts each categorical feature into binary indicator columns, allowing the linear model to learn separate weights for each category. Without this step, the model would either fail to train or treat categorical strings as ordinal values, which is mathematically invalid for linear decision boundaries.
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.
✗
Normalize numerical features
Why it's wrong here
Normalization is helpful but not strictly required; linear models can handle raw values.
✗
Impute missing values
Why it's wrong here
Imputation is important but not the only required step; encoding is mandatory for categoricals.
✗
Remove outliers
Why it's wrong here
Outlier removal is optional and not a required step for linear models.
✓
One-hot encode categorical features
Why this is correct
Linear models require numerical input; one-hot encoding converts categories to binary vectors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume normalization (A) is the most critical step for linear models, overlooking that categorical features must be converted to numerical form before any linear classifier can process them.
Detailed technical explanation
How to think about this question
One-hot encoding creates a binary vector of length equal to the number of unique categories, ensuring no ordinal relationship is imposed. For high-cardinality categorical features, this can lead to the 'curse of dimensionality' and multicollinearity; techniques like target encoding or feature hashing are sometimes used as alternatives. In linear models, the intercept term must be handled carefully when one-hot encoding to avoid the dummy variable trap, where one category is perfectly collinear with the others.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: One-hot encode categorical features — Linear classifiers (e.g., logistic regression, linear SVM) require numerical input and cannot directly process categorical text labels. One-hot encoding converts each categorical feature into binary indicator columns, allowing the linear model to learn separate weights for each category. Without this step, the model would either fail to train or treat categorical strings as ordinal values, which is mathematically invalid for linear decision boundaries.
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.
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Question Discussion
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