The answer is imputing missing values with the median for numerical columns and the mode for categorical columns, combined with adding an indicator variable to flag where values were missing. These two approaches are appropriate during EDA because median imputation is robust to outliers, unlike mean imputation which can skew distributions, while mode preserves the most frequent category without distorting categorical data. The indicator variable is critical because it retains the information that a value was originally missing, allowing the model to potentially learn patterns from the absence itself. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical EDA trade-offs—specifically, avoiding listwise deletion (which reduces sample size and can introduce bias) and avoiding forward fill (which is reserved for time series). A common trap is choosing mean imputation, but remember: median resists outliers, mode fits categories. Memory tip: “Med for num, Mode for cat, and flag the gap.”
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 performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create an indicator column to flag whether the value was missing, then impute with a placeholder
Options C and D are correct because imputation with median/mode is robust, and flagging missingness with an indicator variable preserves information. Option A is wrong because listwise deletion can introduce bias and reduce sample size. Option B is wrong because mean imputation is sensitive to outliers. Option E is wrong because forward fill is for time series, not general EDA.
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.
✗
Use forward fill to propagate the last observed value
Why it's wrong here
Forward fill is appropriate for time series data, not general datasets.
✗
Remove all rows with any missing value (listwise deletion)
Why it's wrong here
Listwise deletion reduces sample size and can introduce bias.
✓
Create an indicator column to flag whether the value was missing, then impute with a placeholder
Why this is correct
This retains the information about missingness and is a common practice.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with the mean of each column
Why it's wrong here
Mean imputation is sensitive to outliers and can distort distributions.
✓
Impute missing values with the median for numerical columns and mode for categorical columns
Why this is correct
Median and mode are robust to outliers and preserve the central tendency.
Related concept
Read the scenario before looking for a memorised answer.
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: Create an indicator column to flag whether the value was missing, then impute with a placeholder — Options C and D are correct because imputation with median/mode is robust, and flagging missingness with an indicator variable preserves information. Option A is wrong because listwise deletion can introduce bias and reduce sample size. Option B is wrong because mean imputation is sensitive to outliers. Option E is wrong because forward fill is for time series, not general EDA.
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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