The correct answer is to use a box plot and the z-score method for identifying outliers. A box plot identifies outliers by visualizing the interquartile range (IQR), where any data point falling more than 1.5 times the IQR below the first quartile or above the third quartile is flagged as an outlier. The z-score method, assuming a roughly normal distribution, flags any point with an absolute z-score greater than 3 as an outlier, since such values are more than three standard deviations from the mean. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of fundamental exploratory data analysis techniques, often appearing in the Data Engineering domain. A common trap is confusing measures of central tendency like mean and median with detection methods, or thinking a histogram alone systematically identifies outliers. Remember the memory tip: “Box plots catch the whiskers, z-scores catch the tails.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 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 D are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles. Z-scores identify outliers as points with |z| > 3 (assuming normal distribution). Option A is wrong because mean and median are measures of central tendency, not outlier detection. Option C is wrong because histograms show distribution shape but do not explicitly identify outliers. Option E is wrong because pairwise scatter plots may show outliers but are not a systematic method.
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
Scatter plot matrix is not a systematic outlier detection method.
✓
Calculate z-scores and flag any data points with |z| > 3.
Why this is correct
Z-scores provide a statistical threshold for outliers.
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
Box plots visually flag outliers.
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 mean and median does not identify specific outliers.
✗
Plot a histogram and look for gaps.
Why it's wrong here
Histograms may indicate outliers but are not a formal 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.
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 z-scores and flag any data points with |z| > 3. — Options B and D are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles. Z-scores identify outliers as points with |z| > 3 (assuming normal distribution). Option A is wrong because mean and median are measures of central tendency, not outlier detection. Option C is wrong because histograms show distribution shape but do not explicitly identify outliers. Option E is wrong because pairwise scatter plots may show outliers but are not a systematic method.
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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