Question 1,196 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is building a time series forecasting model for monthly sales. The data shows strong seasonality with a yearly pattern. They plan to use Amazon Forecast. Which algorithm should they choose?
⚠ Common exam trap
Candidates often choose XGBoost (A) because it is a powerful general-purpose algorithm, but they overlook that Amazon Forecast provides specialized algorithms like DeepAR+ for time series tasks, and XGBoost is not a native Forecast algorithm.
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
✓
DeepAR+
DeepAR+ is purpose-built for time series forecasting with strong seasonality, as it uses recurrent neural networks (RNNs) to capture complex temporal dependencies and automatically models seasonal patterns like yearly cycles. Amazon Forecast natively supports DeepAR+ for such use cases, making it the optimal choice over general-purpose or non-forecasting algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
XGBoost
Why it's wrong here
XGBoost is not specifically designed for time series forecasting.
- ✗
K-means clustering
Why it's wrong here
K-means is for clustering, not forecasting.
- ✓
DeepAR+
Why this is correct
DeepAR+ is designed for time series with seasonality and trends.
- ✗
Linear regression
Why it's wrong here
Linear regression may not capture seasonality well.
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Last reviewed: Jul 4, 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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