The answer is target encoding with cross-validation. This method is the correct choice for handling high cardinality categorical features in linear models because it replaces each category with the mean of the target variable, preserving the feature’s predictive signal without exploding dimensionality. For a binary target and 200 unique countries, one-hot encoding would create 199 dummy variables, causing the curse of dimensionality and sparse feature space, while label encoding imposes false ordinal relationships and frequency encoding ignores the target’s influence. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of encoding trade-offs for linear models under high cardinality—a common trap is choosing one-hot encoding out of habit, forgetting that linear models suffer from too many dummy variables. The key insight is that target encoding leverages the target directly but requires cross-validation to prevent data leakage and overfitting. Memory tip: think “Target Tames Cardinality”—when cardinality is high and the model is linear, target encoding with cross-validation is your go-to.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Target encoding with cross-validation
Option D is correct because target encoding (or mean encoding) replaces each category with the mean of the target, which is suitable for high cardinality in linear models but requires careful validation to avoid overfitting. Option A is wrong because one-hot encoding would create 199 dummy variables, leading to high dimensionality. Option B is wrong because label encoding imposes an arbitrary ordinal relationship. Option C is wrong because frequency encoding may not capture the relationship with the target.
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 with cross-validation
Why this is correct
Target encoding captures the relationship with the target, and cross-validation prevents data leakage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Label encoding
Why it's wrong here
Label encoding assumes an ordinal relationship that does not exist.
✗
Frequency encoding
Why it's wrong here
Frequency encoding uses count but may lose predictive information.
✗
One-hot encoding
Why it's wrong here
One-hot encoding creates too many features, which can cause the curse of dimensionality.
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 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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Target encoding with cross-validation — Option D is correct because target encoding (or mean encoding) replaces each category with the mean of the target, which is suitable for high cardinality in linear models but requires careful validation to avoid overfitting. Option A is wrong because one-hot encoding would create 199 dummy variables, leading to high dimensionality. Option B is wrong because label encoding imposes an arbitrary ordinal relationship. Option C is wrong because frequency encoding may not capture the relationship with the target.
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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Question Discussion
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