The correct answer is that grouping rare categories into a single 'Other' bucket is an effective technique for handling high cardinality categorical features. This approach directly addresses the problem of sparse representations and overfitting, which occur when a categorical variable has too many unique values—such as zip codes or product IDs—relative to the number of training samples. By consolidating infrequent levels into one catch-all group, you reduce dimensionality and noise while preserving the predictive signal from the most common categories. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept often appears in questions about feature engineering and data preprocessing, testing your ability to balance model complexity against generalization. A common trap is assuming one-hot encoding alone is always safe; in reality, it can explode the feature space for high-cardinality variables, making the model unstable. Memory tip: think "Rare to One" — when categories are rare, roll them into one.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Which TWO statements about handling categorical variables in exploratory data analysis are correct? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
When a categorical feature has high cardinality, consider grouping rare categories.
Option A is correct because high-cardinality categorical features can lead to overfitting and sparse representations. Grouping rare categories into a single 'Other' bucket reduces dimensionality and noise, improving model generalization without losing significant predictive signal.
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.
✓
When a categorical feature has high cardinality, consider grouping rare categories.
Why this is correct
Grouping reduces dimensionality and overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Target encoding always avoids data leakage.
Why it's wrong here
Target encoding can leak target information if not cross-validated.
✓
One-hot encoding creates binary columns for each category.
Why this is correct
One-hot encoding represents categories as binary vectors.
Related concept
Read the scenario before looking for a memorised answer.
✗
Label encoding is suitable for nominal categorical variables.
Why it's wrong here
Label encoding imposes an ordinal order, which may mislead models.
✗
Categorical variables should always be dropped if they have many unique values.
Why it's wrong here
They may contain predictive value; consider encoding or grouping.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that label encoding is safe for nominal data, when in fact it imposes an ordinal relationship that can distort model performance.
Detailed technical explanation
How to think about this question
One-hot encoding creates a binary column for each category, producing a sparse matrix that can explode in dimensionality with high cardinality. Target encoding replaces each category with the mean of the target variable for that category, but without proper cross-validation (e.g., using a holdout fold or adding noise), it introduces target leakage and overfitting. In practice, rare category grouping often uses a frequency threshold (e.g., <5% of samples) to consolidate infrequent levels.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: When a categorical feature has high cardinality, consider grouping rare categories. — Option A is correct because high-cardinality categorical features can lead to overfitting and sparse representations. Grouping rare categories into a single 'Other' bucket reduces dimensionality and noise, improving model generalization without losing significant predictive signal.
What should I do if I get this MLS-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO actions are appropriate during exploratory data analysis when you discover that a categorical feature has 50 unique values (high cardinality)?
medium
✓ A.Group rare categories into a single 'Other' category.
B.Apply one-hot encoding to create 50 dummy variables.
C.Apply label encoding to assign integers to each category.
D.Drop the feature entirely.
✓ E.Use feature hashing (hashing trick) to reduce dimensionality.
Why A: Options B and D are correct. B: Grouping rare categories into an 'Other' category reduces cardinality while preserving information. D: Using feature hashing can transform high-cardinality categorical features into a fixed-size vector. Option A is incorrect because one-hot encoding creates many columns, which can be problematic. Option C is incorrect because dropping the feature may lose important information. Option E is incorrect because label encoding implies ordinality, which may not exist.
Last reviewed: Jun 24, 2026
Question Discussion
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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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