The answer is to remove rows with negative Quantity and impute missing CustomerID and ProductID with 'Unknown'. This is correct because negative quantities represent returns, not data errors, so excluding them focuses the analysis on actual purchase behavior for customer segmentation, while imputing categorical identifiers with a placeholder like 'Unknown' preserves 97% of the data without introducing statistical bias. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish between data cleaning for business context versus generic imputation—a common trap is applying mean imputation to categorical fields or treating returns as outliers to remove. Remember the key principle: for categorical missing values, use a placeholder or mode, never the mean; for domain-specific values like negative quantities, let the business goal guide your filter, not statistical rules. A quick memory tip: "Categorical missing? Placeholder wins; negative sales? Filter for spends."
MLS-C01 Exploratory Data Analysis Practice Question
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 is analyzing a dataset of online retail transactions. The dataset contains 500,000 rows and 10 columns: 'TransactionID', 'CustomerID', 'ProductID', 'Quantity', 'UnitPrice', 'TransactionDate', 'PaymentMethod', 'ShippingAddress', 'Country', and 'TotalAmount'. The data scientist loads the data into a SageMaker notebook and performs initial EDA. The data scientist finds that 'UnitPrice' has a range from $0.01 to $10,000, with a mean of $50 and a median of $20. 'Quantity' ranges from -10 to 100, with negative values indicating returns. 'TotalAmount' is calculated as Quantity * UnitPrice. The data scientist also notices that 2% of the 'CustomerID' values are missing, and 1% of 'ProductID' values are missing. There are no missing values in other columns. The data scientist wants to clean the data and prepare it for customer segmentation. Which course of action is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.
Option A is correct because negative quantities are returns and should be removed if the goal is to model purchase behavior, and missing CustomerID and ProductID can be imputed with 'Unknown' to avoid data loss. Option B is wrong because mean imputation for CustomerID is not valid (categorical). Option C is wrong because removing all rows with any missing values would discard 3% of data. Option D is wrong because negative quantities are meaningful as returns, not errors.
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.
✗
Impute missing 'CustomerID' with the mean of 'CustomerID' and missing 'ProductID' with the mode.
Why it's wrong here
CustomerID is categorical; mean is invalid.
✗
Remove all rows with any missing values.
Why it's wrong here
Removing 3% of data may lose valuable information.
✗
Keep negative 'Quantity' and treat them as errors; replace them with the median of positive quantities.
Why it's wrong here
Negative quantities represent returns and should be handled separately.
✓
Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.
Why this is correct
Negative quantities are returns; imputing with 'Unknown' preserves rows.
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: Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'. — Option A is correct because negative quantities are returns and should be removed if the goal is to model purchase behavior, and missing CustomerID and ProductID can be imputed with 'Unknown' to avoid data loss. Option B is wrong because mean imputation for CustomerID is not valid (categorical). Option C is wrong because removing all rows with any missing values would discard 3% of data. Option D is wrong because negative quantities are meaningful as returns, not errors.
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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