- A
ARIMA (Seasonal ARIMA)
Seasonal ARIMA explicitly models seasonality and autocorrelation, ideal for seasonal time series.
- B
Random forest
Why wrong: Random forest can be used but typically requires careful feature engineering and may not extrapolate well.
- C
K-means clustering
Why wrong: K-means is unsupervised clustering, not for forecasting.
- D
Linear regression
Why wrong: Linear regression can model a trend but does not inherently capture seasonality without manual feature engineering.
Question 196 of 1,000
hardMultiple ChoiceObjective-mapped
Forecasting Monthly Sales with Seasonality Using SARIMA
This MLA-C01 practice question tests your understanding of forecast monthly sales that show clear seasonality. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 company wants to forecast monthly sales that show clear seasonality. Which algorithm is most suitable?
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
✓
ARIMA (Seasonal ARIMA)
Seasonal ARIMA (SARIMA) extends ARIMA by explicitly modeling seasonal components through seasonal differencing and seasonal autoregressive/moving average terms, making it the most suitable algorithm for forecasting monthly sales with clear seasonality. It captures both trend and seasonal patterns by incorporating parameters for the seasonal period (e.g., 12 for monthly data) and can handle non-stationary time series.
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 (Seasonal ARIMA)
Why this is correct
Seasonal ARIMA explicitly models seasonality and autocorrelation, ideal for seasonal time series.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Random forest
Why it's wrong here
Random forest can be used but typically requires careful feature engineering and may not extrapolate well.
- ✗
K-means clustering
Why it's wrong here
K-means is unsupervised clustering, not for forecasting.
- ✗
Linear regression
Why it's wrong here
Linear regression can model a trend but does not inherently capture seasonality without manual feature engineering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between time-series-specific algorithms (like ARIMA) and general-purpose machine learning models (like random forest or linear regression), trapping candidates who overlook that seasonal patterns require explicit temporal modeling rather than treating data as independent observations.
Detailed technical explanation
How to think about this question
SARIMA models are denoted as SARIMA(p,d,q)(P,D,Q)_s, where s is the seasonal period (e.g., 12 for monthly data), and the seasonal differencing (D) removes seasonal non-stationarity. Under the hood, the model uses a multiplicative combination of non-seasonal and seasonal AR and MA polynomials, enabling it to capture patterns like yearly peaks in retail sales. In practice, selecting the right order parameters requires analyzing ACF and PACF plots, and the model can be extended with exogenous variables (SARIMAX) for additional drivers.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: ARIMA (Seasonal ARIMA) — Seasonal ARIMA (SARIMA) extends ARIMA by explicitly modeling seasonal components through seasonal differencing and seasonal autoregressive/moving average terms, making it the most suitable algorithm for forecasting monthly sales with clear seasonality. It captures both trend and seasonal patterns by incorporating parameters for the seasonal period (e.g., 12 for monthly data) and can handle non-stationary time series.
What should I do if I get this MLA-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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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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