- A
Group rare categories into a single 'Other' category.
Reduces cardinality and retains data.
- B
Apply label encoding to all categories.
Why wrong: Label encoding may not handle rarity well.
- C
One-hot encode all 500 categories.
Why wrong: Too many dummy variables.
- D
Drop all rows with rare categories.
Why wrong: Loses data.
Question 121 of 1,755
How to Handle Rare Categories in Categorical Features for EDA
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.
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?
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
✓
Group rare categories into a single 'Other' category.
Option A is correct because grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data. Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.
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.
- ✓
Group rare categories into a single 'Other' category.
Why this is correct
Reduces cardinality and retains data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply label encoding to all categories.
Why it's wrong here
Label encoding may not handle rarity well.
- ✗
One-hot encode all 500 categories.
Why it's wrong here
Too many dummy variables.
- ✗
Drop all rows with rare categories.
Why it's wrong here
Loses data.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 a single 'Other' category. — Option A is correct because grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data. Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
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. 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?
easy- A.Apply one-hot encoding to the 'Region' feature.
- B.Remove the 'Region' feature from the dataset.
- ✓ C.Group rare categories into an 'Other' category.
- D.Oversample the minority classes in the target variable.
Why C: Option C is correct because grouping rare categories into an 'Other' category helps manage highly imbalanced categorical features, preventing the model from overemphasizing the dominant category and allowing rare categories to be represented without causing sparse or noisy signals. Option A is incorrect: one-hot encoding does not address the imbalance; it simply creates dummy variables, and rare categories would still be underrepresented. Option B is incorrect: removing the 'Region' feature could discard potentially useful information; the problem is imbalance, not irrelevance. Option D is incorrect: oversampling the minority class targets address target imbalance, not feature imbalance.
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Last reviewed: Jun 20, 2026
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