Quick Answer
The answer is target encoding, which is the best approach for high cardinality categorical features like location with 1,000 unique values. Target encoding for high cardinality categorical features works by replacing each category with the mean of the target variable—in this case, average house price for that location—thereby capturing predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding, which would create 1,000 dummy columns, and the false ordinality of label encoding. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering trade-offs, especially when cardinality is high and the target is continuous. A common trap is choosing label encoding because it seems simpler, but it imposes arbitrary order that misleads the model. Memory tip: think "mean per bin" for target encoding—it shrinks high-cardinality categories into a single, meaningful number.
This MLS-C01 practice question tests your understanding of modeling. 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 building a regression model to predict house prices. The dataset contains features like 'number_of_rooms' (integer), 'sqft' (float), 'location' (categorical with 1000 unique values). Which feature engineering approach is BEST for the 'location' feature?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Remove the feature
Why wrong: Location is likely important for house prices.
Target encoding
Target encoding uses mean target per category, good for high cardinality.
One-hot encoding
Why wrong: One-hot encoding would create 1000 sparse columns.
Label encoding
Why wrong: Label encoding assumes ordinality, which is not appropriate for locations.
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 best approach for the 'location' feature because it has 1,000 unique categories, making one-hot encoding infeasible (would create 1,000 dummy columns) and label encoding inappropriate (imposes arbitrary ordinal relationships). Target encoding replaces each category with the mean of the target variable (house price) for that category, capturing the predictive signal of location while keeping the feature as a single numeric column. This balances model performance with dimensionality and avoids overfitting when regularized (e.g., with smoothing or cross-validation).
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.
Remove the feature
Why it's wrong here
Location is likely important for house prices.
Target encoding
Why this is correct
Target encoding uses mean target per category, good for high cardinality.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encoding
Why it's wrong here
One-hot encoding would create 1000 sparse columns.
Label encoding
Why it's wrong here
Label encoding assumes ordinality, which is not appropriate for locations.
Common exam traps
AWS often tests the trade-off between cardinality and encoding methods, and the trap here is that candidates default to one-hot encoding as the 'standard' categorical encoding without considering the practical infeasibility of high cardinality, or they choose label encoding thinking it is a simple numeric mapping, ignoring the ordinal assumption violation.
Detailed technical explanation
Target encoding works by mapping each category to the average target value in the training data, but it requires careful regularization (e.g., adding a prior or using cross-validation) to prevent data leakage and overfitting — for example, a category with only one house might get an extreme encoded value. In practice, libraries like category_encoders implement smoothing with a global mean and a weight parameter based on category frequency. A real-world scenario is real estate modeling in a city with hundreds of neighborhoods, where target encoding captures fine-grained price differences without exploding feature space.
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.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Target encoding — Target encoding is the best approach for the 'location' feature because it has 1,000 unique categories, making one-hot encoding infeasible (would create 1,000 dummy columns) and label encoding inappropriate (imposes arbitrary ordinal relationships). Target encoding replaces each category with the mean of the target variable (house price) for that category, capturing the predictive signal of location while keeping the feature as a single numeric column. This balances model performance with dimensionality and avoids overfitting when regularized (e.g., with smoothing or cross-validation).
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This MLS-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 MLS-C01 exam.
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