- A
Create a scatter plot matrix to visually inspect.
Why wrong: Creating a scatter plot matrix can help visually inspect for outliers but is not a systematic detection method.
- B
Calculate z-scores and flag any data points with |z| > 3.
Calculating z-scores and flagging points with |z| > 3 is a standard statistical method for outlier detection, assuming the data is roughly normally distributed.
- C
Use a box plot to visualize the interquartile range (IQR) and identify points outside the whiskers.
Using a box plot to visualize the IQR and identifying points outside the whiskers (1.5*IQR) is a common and effective method for outlier detection.
- D
Compare the mean and median of each column.
Why wrong: Comparing the mean and median of each column can indicate skewness but does not directly identify outliers.
- E
Plot a histogram and look for gaps.
Why wrong: Plotting a histogram shows the distribution shape but requires subjective judgment to identify outliers; it is not a systematic method.
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 analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Calculate z-scores and flag any data points with |z| > 3.
Options B and C are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles (option C). Z-scores identify outliers as points with |z| > 3, assuming a roughly normal distribution (option B). Option A (scatter plot matrix) can help visualize outliers but is not a systematic detection method. Option D (comparing mean and median) provides insight into skewness but does not directly flag outliers. Option E (histogram) shows distribution shape but requires subjective judgment to identify outliers.
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.
- ✗
Create a scatter plot matrix to visually inspect.
Why it's wrong here
Creating a scatter plot matrix can help visually inspect for outliers but is not a systematic detection method.
- ✓
Calculate z-scores and flag any data points with |z| > 3.
Why this is correct
Calculating z-scores and flagging points with |z| > 3 is a standard statistical method for outlier detection, assuming the data is roughly normally distributed.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a box plot to visualize the interquartile range (IQR) and identify points outside the whiskers.
Why this is correct
Using a box plot to visualize the IQR and identifying points outside the whiskers (1.5*IQR) is a common and effective method for outlier detection.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Compare the mean and median of each column.
Why it's wrong here
Comparing the mean and median of each column can indicate skewness but does not directly identify outliers.
- ✗
Plot a histogram and look for gaps.
Why it's wrong here
Plotting a histogram shows the distribution shape but requires subjective judgment to identify outliers; it is not a systematic method.
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
Plotting a histogram shows the distribution shape but requires subjective judgment to identify outliers; it is not a systematic method.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 z-scores and flag any data points with |z| > 3. — Options B and C are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles (option C). Z-scores identify outliers as points with |z| > 3, assuming a roughly normal distribution (option B). Option A (scatter plot matrix) can help visualize outliers but is not a systematic detection method. Option D (comparing mean and median) provides insight into skewness but does not directly flag outliers. Option E (histogram) shows distribution shape but requires subjective judgment to identify outliers.
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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Last reviewed: Jun 20, 2026
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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