Question 917 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?
⚠ Common exam trap
AWS often tests the distinction between visual EDA techniques (like ACF plots) and formal statistical tests (like ADF), trapping candidates who confuse diagnostic plots with hypothesis testing for stationarity.
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
✓
Perform the Augmented Dickey-Fuller (ADF) test.
The Augmented Dickey-Fuller (ADF) test is a formal statistical hypothesis test specifically designed to check for stationarity in a time series. It tests the null hypothesis that a unit root is present, indicating non-stationarity, against the alternative of stationarity. This makes it the most appropriate EDA technique for directly assessing stationarity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Plot the autocorrelation function (ACF).
Why it's wrong here
ACF shows correlation but not stationarity.
- ✗
Use time-series cross-validation.
Why it's wrong here
Cross-validation is for model evaluation.
- ✓
Perform the Augmented Dickey-Fuller (ADF) test.
Why this is correct
ADF test formally tests for unit root (non-stationarity).
- ✗
Create a scatter plot of the series against its lag.
Why it's wrong here
Scatter plot is not a formal test.
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Last reviewed: Jun 30, 2026
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