- A
Target encoding with cross-validation
Target encoding captures the relationship with the target, and cross-validation prevents data leakage.
- B
Label encoding
Why wrong: Label encoding assumes an ordinal relationship that does not exist.
- C
Frequency encoding
Why wrong: Frequency encoding uses count but may lose predictive information.
- D
One-hot encoding
Why wrong: One-hot encoding creates too many features, which can cause the curse of dimensionality.
Question 1,051 of 1,755
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 choices
Why each option matters
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
Target encoding with cross-validation (Option A) is the correct choice for this scenario because it replaces each category in the high-cardinality feature 'country' with the mean of the target variable, effectively capturing the relationship with the target while avoiding the curse of dimensionality. Cross-validation is essential to prevent overfitting by computing the target means on out-of-fold data. One-hot encoding (Option D) would create 199 dummy variables, leading to high dimensionality and potential overfitting, making it unsuitable for linear models with limited data. Label encoding (Option B) imposes an arbitrary ordinal relationship that the linear model would misinterpret. Frequency encoding (Option C) may not capture the relationship with the target and could lose predictive power.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 — Target encoding with cross-validation (Option A) is the correct choice for this scenario because it replaces each category in the high-cardinality feature 'country' with the mean of the target variable, effectively capturing the relationship with the target while avoiding the curse of dimensionality. Cross-validation is essential to prevent overfitting by computing the target means on out-of-fold data. One-hot encoding (Option D) would create 199 dummy variables, leading to high dimensionality and potential overfitting, making it unsuitable for linear models with limited data. Label encoding (Option B) imposes an arbitrary ordinal relationship that the linear model would misinterpret. Frequency encoding (Option C) may not capture the relationship with the target and could lose predictive power.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 20, 2026
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