The answer is ARIMA, specifically its seasonal variant SARIMA, because it is the most appropriate algorithm for time series forecasting with trend and seasonality. ARIMA handles trend through its integrated (I) component, which applies differencing to make the data stationary, while the seasonal ARIMA extension adds seasonal differencing and seasonal autoregressive and moving average terms to capture the repeating 12-month pattern. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of when to choose ARIMA over simpler models like linear regression or exponential smoothing—a common trap is to pick a model that ignores seasonality or assumes a non-repeating pattern. Remember that ARIMA’s power lies in its ability to model both trend and seasonal components explicitly, making it ideal for monthly sales data with a clear upward trend and annual cycles. A useful memory tip: think of ARIMA as “Auto-Regressive Integrated Moving Average,” where the “I” removes the trend, and adding an “S” (for Seasonal) captures the repeating cycle.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 building a time series forecasting model for monthly sales data. The scientist has observed that the sales data shows a clear upward trend and a seasonal pattern that repeats every 12 months. Which algorithm would be most appropriate for this task?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ARIMA
ARIMA (Autoregressive Integrated Moving Average) is specifically designed for time series forecasting and can handle both trend and seasonality through its parameters: the 'I' (differencing) removes trend, and seasonal ARIMA (SARIMA) extends it with seasonal differencing and seasonal AR/MA terms to capture the 12-month repeating pattern. This makes it the most appropriate choice for monthly sales data with a clear upward trend and annual seasonality.
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.
✓
ARIMA
Why this is correct
ARIMA (or SARIMA) directly models trend and seasonality in time series data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Random Forest
Why it's wrong here
Random Forest is not inherently designed for time series forecasting and may not capture temporal dependencies.
✗
k-means clustering
Why it's wrong here
k-means is an unsupervised algorithm for clustering, not for forecasting.
✗
Linear regression with time-based features
Why it's wrong here
Linear regression can model trends but struggles with seasonality without explicit features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Linear regression with time-based features (Option D) because they think adding a time index and month dummies is sufficient, but they overlook that ARIMA is purpose-built for time series with autocorrelation and seasonality, while linear regression violates the independence assumption and cannot model the stochastic seasonal patterns without extensive feature engineering.
Detailed technical explanation
How to think about this question
ARIMA models the autocorrelation in the data after differencing to achieve stationarity, using p (autoregressive order), d (differencing order), and q (moving average order). For seasonal data, SARIMA adds seasonal parameters P, D, Q, and m (seasonal period, e.g., m=12 for monthly data), which explicitly models the correlation between observations 12 months apart. In practice, selecting the correct order parameters often requires analyzing ACF and PACF plots or using automated methods like auto-ARIMA.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ARIMA — ARIMA (Autoregressive Integrated Moving Average) is specifically designed for time series forecasting and can handle both trend and seasonality through its parameters: the 'I' (differencing) removes trend, and seasonal ARIMA (SARIMA) extends it with seasonal differencing and seasonal AR/MA terms to capture the 12-month repeating pattern. This makes it the most appropriate choice for monthly sales data with a clear upward trend and annual seasonality.
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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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.
Question Discussion
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