MLS-C01 Modeling Practice Question
A company has a time series forecasting problem with daily sales data. The data shows both trend and seasonality. Which Amazon SageMaker built-in algorithm is most suitable?
⚠ Common exam trap
It's easy for candidates to choose XGBoost (Option D) because it is a powerful general-purpose algorithm, but they overlook that DeepAR is specifically designed for time series forecasting with trend and seasonality, whereas XGBoost requires manual feature engineering (e.g., lag variables, rolling statistics) to capture temporal patterns and does not natively produce probabilistic forecasts.
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 a supervised learning algorithm for time series forecasting that explicitly models both trend and seasonality using recurrent neural networks (RNNs). It is designed to handle multiple related time series, incorporate additional features like holidays or promotions, and produce probabilistic forecasts, making it the most suitable choice for daily sales data with trend and seasonal patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
K-Means
Why it's wrong here
K-Means is an unsupervised clustering algorithm, not for forecasting.
- ✗
Linear Learner
Why it's wrong here
Linear Learner is for regression/classification, not specialized for time series.
- ✓
DeepAR
Why this is correct
DeepAR is a built-in algorithm for time series forecasting that handles trend and seasonality.
- ✗
XGBoost
Why it's wrong here
XGBoost can be used for time series but requires feature engineering; DeepAR is purpose-built.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.