- A
Ordinal encoding
Why wrong: Ordinal encoding assigns arbitrary integers, implying an ordinal relationship that may not exist, misleading linear models.
- B
Target encoding
Target encoding replaces each category with the target mean, handling high cardinality with a single column and capturing predictive signal.
- C
One-hot encoding
Why wrong: One-hot encoding would create over 10,000 binary columns, drastically increasing dimensionality and sparsity.
- D
Frequency encoding
Why wrong: Frequency encoding replaces categories with their frequency, which does not use target information and may lose predictive power.
Question 56 of 1,000
hardMultiple ChoiceObjective-mapped
Encoding High-Cardinality Categorical Features with Target Encoding
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 company is using Amazon SageMaker Data Wrangler to prepare a dataset with over 200 features. The dataset includes a categorical feature with more than 10,000 unique values (high cardinality). The ML engineer wants to transform this feature into a numeric representation suitable for a linear model without increasing dimensionality too much. Which built-in transform in Data Wrangler should the engineer use?
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
Target encoding is the correct choice because it replaces each category with the mean of the target variable for that category, producing a single numeric column that captures predictive signal without exploding dimensionality. This is ideal for high-cardinality features (e.g., >10,000 unique values) when used with linear models, as it avoids the sparsity and multicollinearity issues of one-hot encoding while retaining correlation with the target.
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.
- ✗
Ordinal encoding
Why it's wrong here
Ordinal encoding assigns arbitrary integers, implying an ordinal relationship that may not exist, misleading linear models.
- ✓
Target encoding
Why this is correct
Target encoding replaces each category with the target mean, handling high cardinality with a single column and capturing predictive signal.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding would create over 10,000 binary columns, drastically increasing dimensionality and sparsity.
- ✗
Frequency encoding
Why it's wrong here
Frequency encoding replaces categories with their frequency, which does not use target information and may lose predictive power.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that high-cardinality categorical features must be one-hot encoded, but the trap here is that one-hot encoding explodes dimensionality, while target encoding provides a compact, target-informed numeric representation suitable for linear models.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Data Wrangler's target encoding uses a smoothed version of the target mean (with a prior and global mean) to prevent overfitting, especially for rare categories. A subtle behavior is that it can leak target information if applied before train/test splitting, so Data Wrangler automatically handles this via cross-fold encoding or by fitting on the training set only. In a real-world scenario, such as predicting customer churn with a 'zip code' feature having 15,000 unique values, target encoding preserves the churn rate per zip code as a numeric feature, enabling a logistic regression model to converge quickly.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Target encoding — Target encoding is the correct choice because it replaces each category with the mean of the target variable for that category, producing a single numeric column that captures predictive signal without exploding dimensionality. This is ideal for high-cardinality features (e.g., >10,000 unique values) when used with linear models, as it avoids the sparsity and multicollinearity issues of one-hot encoding while retaining correlation with the target.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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