The answer is label encoding, as it is the most appropriate method for encoding high cardinality categorical features for tree-based models. Tree-based algorithms, such as decision trees and random forests, make splits based on threshold comparisons, so label encoding’s assignment of distinct integer values allows the model to effectively separate categories without inflating the feature space. This contrasts with one-hot encoding, which would explode the dataset into over a thousand binary columns, degrading performance and interpretability. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of how tree models handle categorical data differently from linear models—a common trap is assuming target encoding is safe, but it introduces data leakage unless carefully validated. Remember the memory tip: “Trees love labels, not columns”—label encoding keeps the feature count lean while preserving the model’s ability to find meaningful splits.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Label encoding
Option A is correct because label encoding assigns integer labels to categories, and tree-based models can effectively split on these ordinal-like values without creating a large number of features. Option B (one-hot encoding) would produce too many features. Option C (target encoding) risks data leakage. Option D (binary encoding) creates fewer features than one-hot but still many and may not be as interpretable for trees.
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 certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Label encoding — Option A is correct because label encoding assigns integer labels to categories, and tree-based models can effectively split on these ordinal-like values without creating a large number of features. Option B (one-hot encoding) would produce too many features. Option C (target encoding) risks data leakage. Option D (binary encoding) creates fewer features than one-hot but still many and may not be as interpretable for trees.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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