Quick Answer
The answer is a bar chart. This is the correct choice because a bar chart is specifically designed for visualizing the distribution of categorical data, where each unique category is represented by a discrete bar, making it easy to compare frequencies or proportions across all 100 values. A histogram, in contrast, requires continuous numeric data grouped into bins, so applying it to categorical features would be technically invalid. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of fundamental data visualization principles, often appearing in questions about feature exploration or preprocessing. A common trap is confusing bar charts with histograms when dealing with high-cardinality categorical features—remember that histograms are for numeric distributions, not categories. Memory tip: think "bars for categories, bins for numbers."
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 wants to understand the distribution of a categorical feature with 100 unique values. Which visualization is most appropriate?
Histogram
Why wrong: Histograms are for numerical features.
Bar chart
Bar charts are ideal for displaying categorical frequencies.
Scatter plot
Why wrong: Scatter plots are for two numerical variables.
Pie chart
Why wrong: Pie charts are less effective with many categories and hard to compare.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Bar chart
A bar chart is the most appropriate visualization for displaying the distribution of a categorical feature with 100 unique values because it uses discrete bars to represent the frequency or proportion of each category. Unlike a histogram, which requires continuous numeric bins, a bar chart preserves the distinct categories and allows clear comparison of counts across all 100 levels.
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.
Histogram
Why it's wrong here
Histograms are for numerical features.
Bar chart
Why this is correct
Bar charts are ideal for displaying categorical frequencies.
Related concept
Read the scenario before looking for a memorised answer.
Scatter plot
Why it's wrong here
Scatter plots are for two numerical variables.
Pie chart
Why it's wrong here
Pie charts are less effective with many categories and hard to compare.
Common exam traps
Cisco often tests the distinction between histograms (for continuous data) and bar charts (for categorical data), and candidates mistakenly choose histogram because they confuse 'distribution' with 'numeric distribution' without recognizing the categorical nature of the feature.
Detailed technical explanation
When dealing with high-cardinality categorical features (e.g., 100 unique values), a bar chart can be sorted by frequency to quickly identify dominant categories and long-tail distributions. In exploratory data analysis (EDA), this is often combined with grouping rare categories into an 'Other' bucket to reduce visual clutter, a technique commonly used in feature engineering for machine learning pipelines.
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
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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: Bar chart — A bar chart is the most appropriate visualization for displaying the distribution of a categorical feature with 100 unique values because it uses discrete bars to represent the frequency or proportion of each category. Unlike a histogram, which requires continuous numeric bins, a bar chart preserves the distinct categories and allows clear comparison of counts across all 100 levels.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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