This MLS-C01 practice question tests your understanding of modeling. 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 customer churn. The dataset contains categorical features with high cardinality (e.g., ZIP code, customer ID). Which encoding method is MOST suitable?
One-hot encoding
Why wrong: One-hot encoding creates too many features.
Label encoding
Why wrong: Label encoding implies order which may not exist.
Hashing encoding
Why wrong: Hashing may cause collisions and lose information.
Target encoding
Target encoding captures information without expanding dimensionality.
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 most suitable for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature space dense. This avoids the curse of dimensionality from one-hot encoding and the arbitrary ordinality of label encoding, which can mislead tree-based models.
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 creates too many features.
Label encoding
Why it's wrong here
Label encoding implies order which may not exist.
Hashing encoding
Why it's wrong here
Hashing may cause collisions and lose information.
Target encoding
Why this is correct
Target encoding captures information without expanding dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often choose one-hot encoding as the default for categorical data, failing to recognize that high cardinality makes it impractical, or they pick label encoding assuming it is safe for tree models, but it introduces false ordinality that can degrade performance.
Detailed technical explanation
Target encoding works by computing the mean of the target (e.g., churn rate) for each category, often with smoothing (e.g., adding a global prior) to prevent overfitting on rare categories. Under the hood, it uses a Bayesian approach where the encoded value is a weighted average of the category mean and the global mean, controlled by a smoothing parameter. In real-world churn models, this allows ZIP codes with few customers to still contribute meaningful information without memorizing noise.
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 most suitable for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature space dense. This avoids the curse of dimensionality from one-hot encoding and the arbitrary ordinality of label encoding, which can mislead tree-based models.
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 24, 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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