Quick Answer
The Augmented Dickey-Fuller (ADF) test is the correct EDA technique for checking stationarity of a time series. This formal statistical hypothesis test directly assesses whether a unit root is present, where the null hypothesis assumes non-stationarity and a significant p-value (typically below 0.05) rejects that null in favor of stationarity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept often appears in questions about time-series preprocessing for forecasting models like ARIMA or when evaluating data suitability for algorithms that assume constant statistical properties. A common trap is confusing visual inspection of plots (which is subjective) with the ADF test’s objective statistical rigor—the exam expects you to know the specific test by name. Remember: ADF stands for “Augmented Dickey-Fuller,” and the key memory tip is that you want the p-value to be low to “ditch the Dickey” (reject the unit root), confirming your series is stationary.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?
Plot the autocorrelation function (ACF).
Why wrong: ACF shows correlation but not stationarity.
Use time-series cross-validation.
Why wrong: Cross-validation is for model evaluation.
Perform the Augmented Dickey-Fuller (ADF) test.
ADF test formally tests for unit root (non-stationarity).
Create a scatter plot of the series against its lag.
Why wrong: Scatter plot is not a formal test.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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).
Related concept
Read the scenario before looking for a memorised answer.
Create a scatter plot of the series against its lag.
Why it's wrong here
Scatter plot is not a formal test.
Common exam traps
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.
Trap categories for this question
Command / output trap
ACF shows correlation but not stationarity.
Detailed technical explanation
The ADF test extends the Dickey-Fuller test by including lagged difference terms to account for higher-order autocorrelation, making it robust for complex time series. Under the hood, it estimates a regression model and compares the t-statistic of the lagged level coefficient against critical values from the Dickey-Fuller distribution. In real-world scenarios, failing to check stationarity can lead to spurious regression results, especially in financial or economic data where trends and seasonality are common.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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