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MLA-C01 Practice Question: Forecast monthly sales that show clear seasonality
A company wants to forecast monthly sales that show clear seasonality. Which algorithm is most suitable?
⚠ Common exam trap
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.
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.
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 both trend and repeating seasonal cycles through its seasonal (P, D, Q, s) terms, capturing the monthly sales pattern. Plain ARIMA lacks this seasonal component, so SARIMA satisfies the clear seasonality constraint.
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Random forest
Why it's wrong here
Random forest regression averages tree predictions on tabular features and does not extrapolate time indices or encode periodicity, so seasonal peaks are missed. It is tempting for tabular regression with engineered calendar features, but raw monthly forecasting needs explicit temporal or seasonal modelling.
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K-means clustering
Why it's wrong here
K-means partitions observations into similarity clusters and produces no time-ordered forecast, so it cannot project seasonal sales forward. It is tempting for segmenting customers or products, where grouping unlabelled data is the goal, but forecasting requires a model that captures temporal dependence.
- ✗
Linear regression
Why it's wrong here
Plain linear regression fits a single trend line and cannot represent repeating seasonal cycles without explicit seasonal terms or differencing. It is tempting as a simple baseline for steady trends, but monthly sales with clear seasonality demand a method that models periodic structure directly.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.