The answer is to use value_counts() on the target column or a count plot. These two methods directly display the frequency of each class in a binary target variable, making them the appropriate metrics for visualizing class balance in a dataset. A count plot provides a visual bar chart of class frequencies, while value_counts() returns a numerical series, both revealing whether the classes are evenly distributed or imbalanced. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to identify class imbalance early in exploratory data analysis, a critical step before model training. A common trap is confusing class balance tools with correlation matrices or scatter plots, which assess feature relationships rather than target distribution. Remember: if you need to see how many samples are in each category, think count—either as a plot or a function call.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 exploring a dataset with a binary target variable. Which TWO metrics are appropriate for evaluating the balance of the target classes? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Count plot of the target variable
Options A and D are correct. Count plot and value_counts show class frequencies. Option B is wrong because correlation matrix shows relationships between features. Option C is wrong because scatter plot shows relationship between two numeric variables. Option E is wrong because histogram shows distribution of a continuous variable.
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.
✓
Count plot of the target variable
Why this is correct
Count plot shows frequency of each class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Histogram of a feature
Why it's wrong here
Histogram shows distribution of a single feature, not target.
✗
Scatter plot of two features colored by target
Why it's wrong here
Scatter plot shows feature relationship, not class balance.
✓
value_counts() on the target column
Why this is correct
value_counts() returns counts of unique values.
Related concept
Read the scenario before looking for a memorised answer.
✗
Correlation matrix of all features
Why it's wrong here
Correlation matrix does not show class balance.
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.
Trap categories for this question
Command / output trap
Histogram shows distribution of a single feature, not target.
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: Count plot of the target variable — Options A and D are correct. Count plot and value_counts show class frequencies. Option B is wrong because correlation matrix shows relationships between features. Option C is wrong because scatter plot shows relationship between two numeric variables. Option E is wrong because histogram shows distribution of a continuous variable.
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.
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Question Discussion
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