The answer is to group rare categories into an 'Other' category and use box plots. This approach directly addresses the challenge of high cardinality categorical visualization EDA by reducing the number of distinct levels to a manageable set, allowing the box plots to clearly show the distribution of the continuous target variable across meaningful groups without visual clutter. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to balance information retention with practical visualization constraints—a common trap is assuming you must keep every unique value or resort to complex encoding like one-hot or feature hashing, which are intended for modeling, not exploratory analysis. Remember the memory tip: "Bin the rare, box the rest" to quickly recall that grouping low-frequency categories preserves the signal from frequent ones while making the plot readable.
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.
A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?
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 and use box plots
For high cardinality categorical features, grouping rare categories into an 'Other' category reduces cardinality and allows meaningful visualizations like box plots. Option A is wrong because removing the feature loses information. Option B is wrong because one-hot encoding creates too many columns and is not suitable for visualization. Option D is wrong because visualizing millions of categories is not feasible. Option E is wrong because feature hashing is for modeling, not EDA visualization.
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 an 'Other' category and use box plots
Why this is correct
Grouping reduces cardinality and box plots effectively show relationship with target.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply one-hot encoding and use scatter plots
Why it's wrong here
One-hot encoding creates too many dimensions and scatter plots become unreadable.
✗
Use a bar chart with all categories on x-axis
Why it's wrong here
Bar chart with millions of categories is not interpretable.
✗
Remove the categorical features from analysis
Why it's wrong here
Removing features loses potential information.
✗
Apply feature hashing and visualize the hashed values
Why it's wrong here
Feature hashing is for modeling, not for interpretable EDA.
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 and use box plots — For high cardinality categorical features, grouping rare categories into an 'Other' category reduces cardinality and allows meaningful visualizations like box plots. Option A is wrong because removing the feature loses information. Option B is wrong because one-hot encoding creates too many columns and is not suitable for visualization. Option D is wrong because visualizing millions of categories is not feasible. Option E is wrong because feature hashing is for modeling, not EDA visualization.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.