The correct approach is to define reasonable bounds based on domain knowledge and then filter or cap the outliers. This method is effective because domain knowledge provides a logical, context-driven threshold—such as a valid age range of 0 to 120 years—allowing you to distinguish genuine data from impossible values without discarding useful information or introducing bias. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of data cleaning best practices during exploratory data analysis, where common traps include blindly removing entire columns, imputing with the mean (which skews distributions when outliers are extreme), or relying on standard scaling, which fails to mitigate outlier influence. The key insight is that statistical methods alone cannot replace human judgment when handling outliers with domain knowledge; always ask whether a value is physically or logically possible. Memory tip: “Know your domain, set the range.”
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 performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Define reasonable bounds based on domain knowledge and filter or cap the outliers
Using domain knowledge to define valid age range (e.g., 0-120) and filtering out or capping outliers is the most appropriate approach. Option B is wrong because removing the entire column loses information. Option C is wrong because imputing with mean distorts the distribution if outliers are extreme. Option D is wrong because standard scaling does not handle outliers; it will still be affected.
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.
✗
Apply standard scaling to the 'age' column
Why it's wrong here
Scaling does not remove or correct outliers.
✗
Impute the outlier values with the mean of the column
Why it's wrong here
Imputation with mean is sensitive to extreme outliers.
✓
Define reasonable bounds based on domain knowledge and filter or cap the outliers
Why this is correct
Domain knowledge provides logical bounds to handle outliers appropriately.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove the 'age' column entirely
Why it's wrong here
Removing a potentially important feature loses information.
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: Define reasonable bounds based on domain knowledge and filter or cap the outliers — Using domain knowledge to define valid age range (e.g., 0-120) and filtering out or capping outliers is the most appropriate approach. Option B is wrong because removing the entire column loses information. Option C is wrong because imputing with mean distorts the distribution if outliers are extreme. Option D is wrong because standard scaling does not handle outliers; it will still be affected.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?
hard
A.Impute the negative values with the median of positive transaction amounts.
✓ B.Remove rows with negative transaction amounts from the dataset.
C.Take the absolute value of the negative transaction amounts.
D.Cap the negative values at zero.
Why B: Option D is correct because the negative values are errors and likely distort the distribution; removing them is straightforward and valid. Option A is wrong because taking absolute values would incorrectly treat errors as legitimate high values. Option B is wrong because negative values are not missing, so imputation is not appropriate. Option C is wrong because capping may still retain erroneous values.
Last reviewed: Jun 20, 2026
Question Discussion
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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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