Quick Answer
Target encoding is the correct choice because it replaces each of the 500 neighborhood categories with the mean of the target variable—house price—for that category, capturing the target relationship while adding only one new feature column. This technique is ideal for high-cardinality categorical features because it avoids the massive dimensionality explosion of one-hot encoding, which would create 500 binary columns and degrade model performance. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of encoding trade-offs: you must recognize when to prioritize capturing target signal over preserving category independence, especially in regression tasks. A common trap is choosing one-hot encoding for its simplicity, but that ignores the curse of dimensionality with high cardinality. Remember the memory tip: “Target encoding tames high cardinality by turning categories into averages—one column, one relationship.”
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 is preparing a dataset for a regression model that predicts house prices. The dataset includes a `neighborhood` feature with 500 distinct categories. The data scientist wants to encode this feature without increasing dimensionality too much and while capturing the target relationship. Which encoding technique should be used?
Target encoding (mean encoding)
Target encoding captures target relationship with low dimensionality.
One-hot encoding
Why wrong: One-hot encoding creates excessive dummy variables.
Frequency encoding
Why wrong: Frequency encoding uses counts, not target information.
Label encoding
Why wrong: Label encoding imposes an artificial order.
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 (mean encoding)
Target encoding (mean encoding) is the correct choice because it replaces each of the 500 neighborhood categories with the mean of the target variable (house price) for that category. This captures the relationship between the neighborhood and the target while adding only one new feature column, thus avoiding the massive dimensionality explosion that would occur with one-hot encoding (which would create 500 binary columns).
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.
Target encoding (mean encoding)
Why this is correct
Target encoding captures target relationship with low dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encoding
Why it's wrong here
One-hot encoding creates excessive dummy variables.
Frequency encoding
Why it's wrong here
Frequency encoding uses counts, not target information.
Label encoding
Why it's wrong here
Label encoding imposes an artificial order.
Common exam traps
AWS often tests the trade-off between dimensionality and information retention, and the trap here is that candidates may choose one-hot encoding out of habit, failing to recognize that 500 categories make it impractical, or choose label encoding because it seems simple, ignoring the ordinal assumption it imposes.
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. Under the hood, this is equivalent to a simple Bayesian approach: for each category, the encoded value is a weighted average of the category's target mean and the global target mean, with weights based on category frequency. In real-world scenarios, this technique is common in high-cardinality categorical features (e.g., zip codes, user IDs) where one-hot encoding is infeasible, but it requires careful cross-validation to avoid target leakage.
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 (mean encoding) — Target encoding (mean encoding) is the correct choice because it replaces each of the 500 neighborhood categories with the mean of the target variable (house price) for that category. This captures the relationship between the neighborhood and the target while adding only one new feature column, thus avoiding the massive dimensionality explosion that would occur with one-hot encoding (which would create 500 binary columns).
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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