The correct answer is to group rare categories into an 'Other' category. This approach directly addresses the problem of handling rare categories in categorical features, where a single level dominates the distribution—like a region accounting for 80% of the data—causing the model to treat infrequent levels as noise or fail to learn meaningful patterns. By collapsing these sparse categories into a single 'Other' bin, you reduce dimensionality and prevent overfitting, allowing the model to focus on the predictive signal from the majority category without discarding the feature entirely. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering for imbalanced categorical data, a common pitfall distinct from target imbalance. A frequent trap is confusing feature imbalance with target imbalance and reaching for oversampling (Option D), but remember: oversampling fixes a skewed target, not a skewed predictor. Memory tip: "Rare in the feature, bin to 'Other'—don't drop, don't oversample."
MLS-C01 Exploratory Data Analysis Practice Question
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 data scientist is working on a project to predict customer churn. The dataset contains 50,000 rows and 20 features, including categorical variables like 'Region' (10 categories) and 'SubscriptionType' (5 categories). The target variable is binary (churn or not). During exploratory data analysis, they plot the distribution of each feature and notice that 'Region' has a highly imbalanced distribution: one region accounts for 80% of the data. Which of the following is the most appropriate next step?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Group rare categories into an 'Other' category.
Option B is correct because imbalanced categorical features may cause the model to ignore rare categories; grouping rare levels into an 'Other' category can improve model performance. Option A is wrong because removing the feature could discard useful information. Option C is wrong because one-hot encoding does not address imbalance. Option D is wrong because oversampling addresses target imbalance, not feature imbalance.
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.
✗
Apply one-hot encoding to the 'Region' feature.
Why it's wrong here
One-hot encoding does not solve imbalance; it creates many sparse columns.
✗
Remove the 'Region' feature from the dataset.
Why it's wrong here
Removing the feature may discard valuable information; rare categories might still be predictive.
✓
Group rare categories into an 'Other' category.
Why this is correct
This reduces sparsity and helps the model learn patterns for rare categories.
Related concept
Read the scenario before looking for a memorised answer.
✗
Oversample the minority classes in the target variable.
Why it's wrong here
Oversampling addresses target class imbalance, not feature imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
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: Group rare categories into an 'Other' category. — Option B is correct because imbalanced categorical features may cause the model to ignore rare categories; grouping rare levels into an 'Other' category can improve model performance. Option A is wrong because removing the feature could discard useful information. Option C is wrong because one-hot encoding does not address imbalance. Option D is wrong because oversampling addresses target imbalance, not feature imbalance.
What should I do if I get this MLS-C01 question wrong?
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.
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. During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?
easy
✓ A.Group rare categories into a single 'Other' category.
B.Apply label encoding to all categories.
C.One-hot encode all 500 categories.
D.Drop all rows with rare categories.
Why A: Option D is correct because grouping rare categories into 'Other' reduces cardinality. Option A is wrong because one-hot encoding all 500 creates many features. Option B is wrong because dropping all may lose information. Option C is wrong because label encoding rare categories may not help.
Last reviewed: Jun 20, 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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