Quick Answer
The answer is ordinal encoding. Tree-based models, such as decision trees and random forests, split data based on feature thresholds, so they naturally interpret ordinal encoding—where each unique category is assigned a distinct integer—without assuming any false mathematical relationship between the values. This makes ordinal encoding ideal for high cardinality features because it avoids the dimensionality explosion of one-hot encoding and the overfitting risk of target encoding. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how tree-based algorithms handle categorical data differently from linear models; a common trap is choosing label encoding, which can impose an unintended ordinal relationship, or target encoding, which leaks target information. Remember the memory tip: “Trees love order, not one-hot oceans”—ordinal encoding keeps cardinality compact while preserving the split logic trees rely on.
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 working with a dataset containing categorical features with high cardinality. The scientist wants to use a tree-based model. Which encoding method should be used?
Ordinal encoding
Ordinal encoding assigns integers without implying order, suitable for trees.
Target encoding
Why wrong: Target encoding can lead to overfitting.
Label encoding
Why wrong: Label encoding may imply order.
One-hot encoding
Why wrong: One-hot encoding creates many sparse 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
Ordinal encoding
Option C is correct because tree-based models can handle ordinal encoding naturally. Option A is wrong because one-hot encoding creates many dimensions, not ideal for high cardinality. Option B is wrong because label encoding may impose ordinal relationship. Option D is wrong because target encoding may cause overfitting.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Ordinal encoding
Why this is correct
Ordinal encoding assigns integers without implying order, suitable for trees.
Related concept
Static NAT maps one inside address to one outside address.
Target encoding
Why it's wrong here
Target encoding can lead to overfitting.
Label encoding
Why it's wrong here
Label encoding may imply order.
One-hot encoding
Why it's wrong here
One-hot encoding creates many sparse features.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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FAQ
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
The correct answer is: Ordinal encoding — Option C is correct because tree-based models can handle ordinal encoding naturally. Option A is wrong because one-hot encoding creates many dimensions, not ideal for high cardinality. Option B is wrong because label encoding may impose ordinal relationship. Option D is wrong because target encoding may cause overfitting.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 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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