This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 machine learning engineer is preparing a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). Which technique is most appropriate for encoding these high-cardinality categorical features?
Label encoding
Why wrong: Assumes ordinal relationship, not suitable for nominal categories.
One-hot encoding
Why wrong: Creates too many features, leading to high dimensionality.
Frequency encoding
Why wrong: Replaces with count/frequency, may lose information.
Target encoding
Encodes using target mean, handles high cardinality well.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Target encoding
Target encoding is the most appropriate technique for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordinality of label encoding, making it a common choice in gradient boosting frameworks like XGBoost or LightGBM for datasets with thousands of unique categories.
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
Assumes ordinal relationship, not suitable for nominal categories.
One-hot encoding
Why it's wrong here
Creates too many features, leading to high dimensionality.
Frequency encoding
Why it's wrong here
Replaces with count/frequency, may lose information.
Target encoding
Why this is correct
Encodes using target mean, handles high cardinality well.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the misconception that one-hot encoding is always the safest choice for categorical data, but candidates fail to recognize that high cardinality makes it impractical, leading them to overlook target encoding as a more efficient alternative.
Detailed technical explanation
Target encoding works by computing the mean of the target for each category, often with smoothing (e.g., adding a global mean prior) to prevent overfitting on rare categories. In practice, this is implemented using cross-validation schemes to avoid data leakage, where the encoding for a training row is calculated from out-of-fold data. A real-world scenario is encoding ZIP codes in a real estate price prediction model, where target encoding captures localized price trends without creating thousands of dummy variables.
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: Target encoding — Target encoding is the most appropriate technique for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordinality of label encoding, making it a common choice in gradient boosting frameworks like XGBoost or LightGBM for datasets with thousands of unique categories.
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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