Quick Answer
The correct answer is STL, or Seasonal and Trend decomposition using Loess, because it is the only method among the options designed specifically to separate trend, seasonal, and residual components while remaining robust to outliers and flexible with any seasonality period, such as the weekly pattern in daily website traffic data. Unlike classical decomposition, which assumes a fixed additive or multiplicative structure and can be distorted by anomalies, STL uses locally weighted regression to iteratively extract each component, making it ideal for real-world datasets with noise or irregular spikes. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between decomposition methods and forecasting models—a common trap is confusing ARIMA or ETS, which are used for prediction, with decomposition techniques that analyze historical patterns. Remember the memory tip: STL stands for “Separate Trend and Loess,” reinforcing that it isolates components using robust local smoothing.
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.
A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?
Additive decomposition using moving averages.
Why wrong: Classical decomposition is less robust to outliers.
Use STL (Seasonal and Trend decomposition using Loess).
STL is robust and flexible for any seasonality.
Fit an ARIMA model and examine residuals.
Why wrong: ARIMA is for forecasting, not decomposition.
Apply an ETS (Error, Trend, Seasonal) model.
Why wrong: ETS is a forecasting model, not a decomposition method.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use STL (Seasonal and Trend decomposition using Loess).
Option C is correct because STL decomposition is robust to outliers and can handle any seasonality period, making it suitable for daily data with weekly seasonality. Option A is wrong because classical decomposition assumes additive seasonality and is less robust. Option B is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing model, not primarily for decomposition.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Additive decomposition using moving averages.
Why it's wrong here
Classical decomposition is less robust to outliers.
Use STL (Seasonal and Trend decomposition using Loess).
Why this is correct
STL is robust and flexible for any seasonality.
Related concept
Static NAT maps one inside address to one outside address.
Fit an ARIMA model and examine residuals.
Why it's wrong here
ARIMA is for forecasting, not decomposition.
Apply an ETS (Error, Trend, Seasonal) model.
Why it's wrong here
ETS is a forecasting model, not a decomposition method.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
The correct answer is: Use STL (Seasonal and Trend decomposition using Loess). — Option C is correct because STL decomposition is robust to outliers and can handle any seasonality period, making it suitable for daily data with weekly seasonality. Option A is wrong because classical decomposition assumes additive seasonality and is less robust. Option B is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing model, not primarily for decomposition.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 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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