The answer is target encoding. For high-cardinality categorical features in linear models, target encoding replaces each category with the mean of the target variable, capturing predictive signal without exploding the feature space. One-hot encoding would create thousands of binary columns, leading to the curse of dimensionality and sparse data, while label or ordinal encoding assigns arbitrary integers that linear models mistakenly treat as ordered relationships. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of feature engineering trade-offs—especially that linear models cannot handle high-dimensional sparse inputs efficiently. A common trap is choosing label encoding because it seems simple, but remember that linear models assume monotonic relationships, so arbitrary integers distort the model’s interpretation. Memory tip: “Target encoding tames high cardinality by turning categories into target averages—no extra columns, no false order.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Target encoding
One-hot encoding creates many binary columns, which can cause the curse of dimensionality for high-cardinality features. Label encoding assigns arbitrary integers, which linear models may interpret as ordinal. Target encoding (mean encoding) replaces categories with the mean of the target variable, which captures information without expanding dimensionality. This is often used for high-cardinality features. Ordinal encoding is similar to label 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.
✓
Target encoding
Why this is correct
Target encoding replaces categories with the mean of the target variable, reducing dimensionality and capturing predictive power.
Related concept
Read the scenario before looking for a memorised answer.
✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers, which linear models may interpret as ordinal relationships.
✗
One-hot encoding
Why it's wrong here
One-hot encoding creates many features, which can lead to high dimensionality and sparsity.
✗
Ordinal encoding
Why it's wrong here
Ordinal encoding is similar to label encoding and not suitable for non-ordinal categories.
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.
Trap categories for this question
Similar concept trap
Ordinal encoding is similar to label encoding and not suitable for non-ordinal categories.
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 MLS-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.
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: Target encoding — One-hot encoding creates many binary columns, which can cause the curse of dimensionality for high-cardinality features. Label encoding assigns arbitrary integers, which linear models may interpret as ordinal. Target encoding (mean encoding) replaces categories with the mean of the target variable, which captures information without expanding dimensionality. This is often used for high-cardinality features. Ordinal encoding is similar to label encoding.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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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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.
Question Discussion
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