- A
One-hot encoding
Why wrong: One-hot encoding creates a new column per category, leading to extremely high dimensionality.
- B
Label encoding
Label encoding converts categories to integers, which tree-based models can handle without expanding feature space.
- C
Target encoding
Why wrong: Target encoding uses the target variable to encode categories, which can cause data leakage and overfitting.
- D
Binary encoding
Why wrong: Binary encoding reduces feature count but still creates many columns and may not be optimal for tree splits.
Question 37 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is performing feature…
This MLA-C01 practice question tests your understanding of a data scientist is performing feature…. 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 performing feature engineering on a dataset containing a categorical feature with high cardinality (over 1000 unique values). Which encoding method is most appropriate to use as input for a tree-based model?
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
✓
Label encoding
Label encoding is most appropriate for tree-based models because it assigns a unique integer to each category without creating a high-dimensional sparse matrix. Tree-based models can handle ordinal-like integer encodings effectively, as they split on feature values based on thresholds, and label encoding preserves the cardinality in a single column without the memory explosion of one-hot encoding.
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.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates a new column per category, leading to extremely high dimensionality.
- ✓
Label encoding
Why this is correct
Label encoding converts categories to integers, which tree-based models can handle without expanding feature space.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Target encoding
Why it's wrong here
Target encoding uses the target variable to encode categories, which can cause data leakage and overfitting.
- ✗
Binary encoding
Why it's wrong here
Binary encoding reduces feature count but still creates many columns and may not be optimal for tree splits.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many candidates assume one-hot encoding is always the safest choice for categorical data, but for tree-based models with high cardinality, label encoding is preferred to avoid the dimensionality explosion and sparsity that degrade performance.
Detailed technical explanation
How to think about this question
Tree-based models like Random Forest or XGBoost make splits based on feature values; label encoding assigns integers that preserve the natural ordering only if the categories are ordinal, but for nominal categories, the model can still find splits because trees are invariant to monotonic transformations. In practice, label encoding for high cardinality features is often combined with feature importance analysis to prune rare categories, and some implementations like CatBoost offer built-in handling for categorical features using ordered target statistics to avoid leakage.
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 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
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Label encoding — Label encoding is most appropriate for tree-based models because it assigns a unique integer to each category without creating a high-dimensional sparse matrix. Tree-based models can handle ordinal-like integer encodings effectively, as they split on feature values based on thresholds, and label encoding preserves the cardinality in a single column without the memory explosion of one-hot encoding.
What should I do if I get this MLA-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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Last reviewed: Jul 4, 2026
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