Quick Answer
The answer is one-hot encoding. This technique is the correct choice for categorical encoding of nominal data like a 'Country' column because it creates a separate binary feature for each of the 50 unique values, ensuring that no ordinal relationship is imposed between categories. In linear regression, the model interprets numerical inputs as having a meaningful order, so using label encoding would incorrectly imply that Country 1 is greater than Country 2; one-hot encoding avoids this by treating each country as an independent, non-ranked category. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of how to handle categorical variables without introducing bias into linear models—a common trap is assuming label encoding is acceptable for nominal data. Remember the memory tip: “One-hot for no rank, label for an order bank.”
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 needs to convert categorical variables to numerical format for a linear regression model. The dataset contains a 'Country' column with 50 unique values. Which transformation should the engineer use to avoid introducing ordinal relationships?
Label encoding
Why wrong: Label encoding assigns arbitrary ordinal values, which can be misinterpreted by linear models.
Target encoding
Why wrong: Target encoding uses the target variable to encode categories, risking data leakage and overfitting.
One-hot encoding
Correct because it creates binary columns without ordinality.
Ordinal encoding
Why wrong: Ordinal encoding implies an order that does not exist for countries.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
One-hot encoding
One-hot encoding is correct because it creates binary columns for each category, avoiding any implicit ordinal relationship between the 50 unique countries. This is essential for linear regression, which assumes numerical inputs have meaningful order; one-hot encoding ensures the model treats each country as an independent category without ranking.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Label encoding
Why it's wrong here
Label encoding assigns arbitrary ordinal values, which can be misinterpreted by linear models.
Target encoding
Why it's wrong here
Target encoding uses the target variable to encode categories, risking data leakage and overfitting.
One-hot encoding
Why this is correct
Correct because it creates binary columns without ordinality.
Related concept
Read the scenario before looking for a memorised answer.
Ordinal encoding
Why it's wrong here
Ordinal encoding implies an order that does not exist for countries.
Common exam traps
AWS often tests the distinction between label encoding and one-hot encoding, trapping candidates who assume integer mapping is harmless for linear models without recognizing the ordinal bias it introduces.
Detailed technical explanation
One-hot encoding creates k-1 binary dummy variables for k categories to avoid multicollinearity in linear regression (the dummy variable trap). Under the hood, the design matrix becomes sparse, and regularization techniques like Ridge or Lasso can help handle the increased dimensionality when k is large (e.g., 50 countries). In real-world scenarios, high-cardinality features may require dimensionality reduction or feature hashing to maintain model performance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. 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.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: One-hot encoding — One-hot encoding is correct because it creates binary columns for each category, avoiding any implicit ordinal relationship between the 50 unique countries. This is essential for linear regression, which assumes numerical inputs have meaningful order; one-hot encoding ensures the model treats each country as an independent category without ranking.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This MLA-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 MLA-C01 exam.
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