Question 235 of 1,672
MLS-C01 Modeling Practice Question
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?
⚠ Common exam trap
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.
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
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.
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.
- ✗
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.
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Last reviewed: Jun 24, 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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