The answer is to visualize the distribution of each feature using histograms and report the count of missing values per column. These two actions are foundational EDA steps for datasets with missing values and outliers because histograms immediately reveal the shape of the data, highlighting skewness and potential outlier tails, while reporting missing value counts quantifies data completeness before any imputation or removal decisions are made. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that EDA must precede data cleaning—a common trap is jumping to imputation or outlier removal without first understanding the data’s structure. Remember that visualizing distributions and counting missing values are diagnostic, not corrective, steps. A useful memory tip: “See the shape, count the gaps” to recall that histograms show shape and missing value counts reveal gaps, keeping you from prematurely fixing what you haven’t yet explored.
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.
Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Calculate the percentage of missing values per column.
Options B and E are correct. B: Visualizing the distribution helps identify shape and outliers. E: Reporting missing value counts is a standard EDA step. A is wrong because imputation should be done after analysis. C is wrong because removing outliers without analysis is premature. D is wrong because normalization is not a first step.
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.
✓
Calculate the percentage of missing values per column.
Why this is correct
Missing value counts inform imputation strategy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Normalize all features using Min-Max scaling.
Why it's wrong here
Normalization is a preprocessing step, not initial EDA.
✗
Remove all rows with outliers.
Why it's wrong here
Outliers may be valid; removal should be justified.
✗
Impute missing values with the mean immediately.
Why it's wrong here
Imputation should be decided after understanding the data.
✓
Visualize the distribution of each feature using histograms.
Why this is correct
Visualization reveals data shape and potential outliers.
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: Calculate the percentage of missing values per column. — Options B and E are correct. B: Visualizing the distribution helps identify shape and outliers. E: Reporting missing value counts is a standard EDA step. A is wrong because imputation should be done after analysis. C is wrong because removing outliers without analysis is premature. D is wrong because normalization is not a first step.
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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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.
Question Discussion
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