Quick Answer
The answer is count encoding. This method is most appropriate for high-cardinality categorical features because it replaces each category with its frequency count, effectively capturing information for rare values without expanding the feature space or introducing the risk of overfitting. When encoding high-cardinality categorical features to avoid overfitting, count encoding avoids the dimensionality explosion of one-hot encoding—which would create 1,000 columns—and sidesteps the overfitting trap of target encoding, which can memorize rare categories. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of encoding trade-offs under the Feature Engineering domain; a common trap is choosing target encoding for its predictive power, but the long tail of singleton values makes it prone to leakage. Remember the mnemonic: “Count the crowd, not the target”—use frequency counts for rare categories to keep dimensionality low and generalization high.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 team is analyzing a dataset with many categorical features. They notice that one feature has 1,000 unique values but a long tail where most values appear only once. Which encoding method is most appropriate to avoid overfitting?
Target encoding
Why wrong: Target encoding can cause overfitting, especially with rare categories.
Label encoding
Why wrong: Label encoding imposes ordinal relationships that may not exist.
One-hot encoding
Why wrong: One-hot encoding would create too many features and lead to sparsity.
Count encoding
Count encoding replaces categories with their frequency, reducing dimensionality and handling rare values.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Count encoding
Option C is correct because count encoding uses frequency counts, which can capture information for rare categories without creating high dimensionality. One-hot encoding (A) would create 1,000 columns. Target encoding (B) can cause overfitting. Label encoding (D) implies ordinality.
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
Why it's wrong here
Target encoding can cause overfitting, especially with rare categories.
Label encoding
Why it's wrong here
Label encoding imposes ordinal relationships that may not exist.
One-hot encoding
Why it's wrong here
One-hot encoding would create too many features and lead to sparsity.
Count encoding
Why this is correct
Count encoding replaces categories with their frequency, reducing dimensionality and handling rare values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Count encoding — Option C is correct because count encoding uses frequency counts, which can capture information for rare categories without creating high dimensionality. One-hot encoding (A) would create 1,000 columns. Target encoding (B) can cause overfitting. Label encoding (D) implies ordinality.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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