This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 has 20 features, including categorical variables with high cardinality (e.g., ZIP code). The data scientist wants to use a linear model. Which feature engineering technique is MOST appropriate for the high-cardinality categorical features?
One-hot encoding
Why wrong: One-hot encoding leads to high dimensionality.
Target encoding
Target encoding handles high cardinality well.
TF-IDF
Why wrong: TF-IDF is for text data.
Standard scaling
Why wrong: Scaling is for numerical features.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Target encoding
Target encoding is the most appropriate technique for high-cardinality categorical features when using a linear model because it replaces each category with the mean of the target variable for that category, creating a numeric feature that captures the relationship between the category and the target without exploding the feature space. One-hot encoding would create an unmanageable number of binary columns (e.g., thousands of ZIP codes), leading to the curse of dimensionality and making the linear model unstable or computationally infeasible.
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.
One-hot encoding
Why it's wrong here
One-hot encoding leads to high dimensionality.
Target encoding
Why this is correct
Target encoding handles high cardinality well.
Related concept
Read the scenario before looking for a memorised answer.
TF-IDF
Why it's wrong here
TF-IDF is for text data.
Standard scaling
Why it's wrong here
Scaling is for numerical features.
Common exam traps
AWS often tests the trap that candidates default to one-hot encoding for all categorical variables, failing to recognize that high cardinality makes it impractical for linear models, whereas target encoding is a more efficient alternative that preserves feature information without dimensionality explosion.
Detailed technical explanation
Target encoding works by mapping each category to the mean of the target (e.g., churn rate) for that category, often with smoothing (e.g., adding a global mean prior) to avoid overfitting on rare categories. Under the hood, this creates a single numeric feature that preserves the predictive signal of the high-cardinality variable, but it introduces target leakage if applied before train-test splitting, so cross-validation or separate encoding on folds is critical. In a real-world scenario, a ZIP code with only a few customers might have an extreme churn rate due to noise, so smoothing with a global mean (e.g., using a formula like (n_i * mean_i + m * global_mean) / (n_i + m)) prevents the model from overfitting to sparse categories.
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.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Target encoding — Target encoding is the most appropriate technique for high-cardinality categorical features when using a linear model because it replaces each category with the mean of the target variable for that category, creating a numeric feature that captures the relationship between the category and the target without exploding the feature space. One-hot encoding would create an unmanageable number of binary columns (e.g., thousands of ZIP codes), leading to the curse of dimensionality and making the linear model unstable or computationally infeasible.
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 30, 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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