The answer is to use visualization techniques like box plots to identify outliers and apply a log transformation to reduce their influence. Box plots are effective for spotting extreme values visually, while a log transformation compresses the data range, making right-skewed distributions more normal without removing data points entirely. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of exploratory data analysis as a preprocessing step—specifically how to handle outliers without discarding information, which is critical for models sensitive to skewed data. A common trap is assuming outliers must always be removed; instead, the exam emphasizes transformations that preserve data integrity. Remember the mnemonic “Box and Log” to pair the detection method with the treatment technique, ensuring you avoid the deletion pitfall.
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 are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply log transformation to reduce the impact of extreme values.
Option B is correct because applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.
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.
✗
Replace the mean with the median for numerical features.
Why it's wrong here
This does not handle outliers directly; consider transformations or robust methods.
✓
Apply log transformation to reduce the impact of extreme values.
Why this is correct
Log transformation can compress skewed distributions and reduce outlier influence.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove all outliers without further investigation.
Why it's wrong here
Outliers may be valid data points; investigate before removal.
✓
Use visualization techniques like box plots to identify outliers.
Why this is correct
Visualizations help understand the distribution and identify outliers.
Related concept
Read the scenario before looking for a memorised answer.
✗
Assume outliers are errors and delete them.
Why it's wrong here
Outliers may be genuine; deleting without analysis can lead to loss of information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data transformation techniques (like log transformation) and data removal or replacement strategies, trapping candidates who think that simply changing a summary statistic (mean to median) or deleting outliers without investigation is a proper handling method.
Detailed technical explanation
How to think about this question
Log transformation is a monotonic transformation that applies the natural logarithm (or base 10) to each data point, which is particularly effective for data with multiplicative relationships or exponential growth. Under the hood, it stabilizes variance and can help meet the homoscedasticity assumption in linear models. In a real-world scenario, such as analyzing income data where a few individuals have extremely high earnings, log transformation can make the distribution more symmetric and reduce the leverage of those outliers in regression analysis.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Apply log transformation to reduce the impact of extreme values. — Option B is correct because applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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