MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist notices that a numeric feature 'age' has outliers beyond 3 standard deviations. What is the most appropriate first step?
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
✓
Investigate the source of the outliers
The most appropriate first step when encountering outliers is to investigate their source (Option D). Outliers could indicate data entry errors, measurement issues, or genuine rare events. Blindly removing them (Option C) or transforming them (Option B) without understanding the cause may distort analysis. Using the feature as-is (Option A) can bias models if outliers are erroneous. Investigation should precede any action.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the feature as-is in the model
Why it's wrong here
Outliers can skew model results.
- ✗
Apply a log transformation to the feature
Why it's wrong here
Log transformation compresses the range but does not address outlier cause.
- ✗
Remove all rows with outlier values
Why it's wrong here
Removing outliers without understanding can discard valid data.
- ✓
Investigate the source of the outliers
Why this is correct
Understanding outliers guides proper handling.
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Same concept, more angles
1 more way this is tested on MLS-C01
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. During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?
easy- A.Remove all rows with age > 100
- ✓ B.Compute summary statistics (min, max, percentiles)
- C.Apply log transformation to normalize the distribution
- D.Impute age values outside 18-100 with the mean
- ✓ E.Create a box plot to visualize outliers
Why B: Computing summary statistics (min, max, percentiles) helps identify the range and potential outliers in the 'age' feature. Option E is correct because a box plot visualizes the distribution and clearly shows outliers, allowing the data scientist to investigate further. Option A is incorrect because removing rows with age > 100 without understanding the context may discard valid data (e.g., errors or special cases). Option C is incorrect because log transformation changes the scale but does not help in identifying outliers; it is used to handle skewed distributions. Option D is incorrect because imputing age values outside 18-100 with the mean would distort the distribution and is not appropriate for investigating outliers; it should only be considered after understanding the nature of the outliers.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.