The answer is label encoding, as it is the most suitable approach for handling high-cardinality categorical features in tree-based models. Unlike one-hot encoding, which creates a massive number of binary columns and leads to dimensionality explosion, label encoding assigns a unique integer to each category without introducing a distance metric. Tree-based models, such as XGBoost or Random Forest, split on feature values and can naturally handle these ordinal integers, since they do not assume linear relationships between categories. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept often appears in questions about feature engineering for tabular data, testing your understanding that tree-based models are invariant to monotonic transformations. A common trap is defaulting to one-hot encoding for all categorical data, but for high-cardinality features, that wastes memory and degrades performance. Remember the memory tip: “Trees don’t care about order, only splits—label encoding keeps it tight.”
MLS-C01 Modeling Practice Question
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 using Amazon SageMaker to train a model on a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., postal codes, product IDs). Which feature engineering approach is most suitable for handling these high-cardinality categorical features in a tree-based model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use label encoding
Label encoding is suitable for tree-based models because these models split on feature values and can handle ordinal relationships implicitly. Unlike linear models, tree-based models do not assume any distancemetric between categories, so label encoding avoids the dimensionality explosion of one-hot encoding while preserving the ability to capture splits based on high-cardinality features.
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 encode the categorical features
Why it's wrong here
One-hot encoding high-cardinality features creates a huge number of dummy variables, leading to high memory usage and sparse data.
✓
Use label encoding
Why this is correct
Tree-based models like XGBoost can effectively use label-encoded features because they make splits based on ordering.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply target encoding
Why it's wrong here
Target encoding (mean of target per category) can introduce target leakage and overfitting if not done with cross-validation.
✗
Apply binary encoding
Why it's wrong here
Binary encoding is possible but not as directly supported; tree models can work with label encoding natively.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to one-hot encoding for categorical features without considering the model type, failing to recognize that tree-based models can effectively use label encoding for high-cardinality features without the drawbacks of dimensionality explosion.
Detailed technical explanation
How to think about this question
Tree-based models (e.g., XGBoost, Random Forest) make splits based on threshold comparisons, so label encoding maps each category to an integer that the tree can use to separate data. However, label encoding can inadvertently imply an ordinal relationship that doesn't exist, but tree-based models are robust to this because they can split on any value and do not rely on distance metrics. In practice, for very high cardinality (e.g., >1000 categories), label encoding is often preferred over one-hot encoding to avoid the curse of dimensionality, and SageMaker's built-in XGBoost algorithm handles label-encoded categorical features efficiently.
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.
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: Use label encoding — Label encoding is suitable for tree-based models because these models split on feature values and can handle ordinal relationships implicitly. Unlike linear models, tree-based models do not assume any distance metric between categories, so label encoding avoids the dimensionality explosion of one-hot encoding while preserving the ability to capture splits based on high-cardinality features.
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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