MLS-C01 Modeling Practice Question
A company has a time series dataset of daily sales for the past 5 years. They want to forecast sales for the next 30 days. The data shows weekly seasonality and a slight upward trend. Which Amazon SageMaker algorithm is most appropriate for this task?
⚠ Common exam trap
The trap here is that candidates often pick XGBoost (Option C) because it is a powerful tree-based model, but they overlook that it lacks native time series capabilities and requires manual feature engineering to capture seasonality and trend, whereas DeepAR is the only option specifically designed for this forecasting task.
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 seasonal patterns and trends. It uses a recurrent neural network (RNN) to model the conditional distribution of future values given past observations, and it natively handles multiple time series, missing data, and known seasonal periods (e.g., weekly). The weekly seasonality and upward trend in the daily sales data are exactly the kind of patterns DeepAR is designed to capture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
DeepAR
Why this is correct
DeepAR is a built-in SageMaker algorithm for time series forecasting that handles seasonality and trends.
- ✗
Linear Learner
Why it's wrong here
Linear Learner is for general regression/classification, not inherently designed for time series forecasting with seasonality.
- ✗
XGBoost
Why it's wrong here
XGBoost is a gradient boosted decision tree algorithm designed for regression and classification tasks on tabular data. It lacks an inherent mechanism to model temporal dependencies or seasonal cycles within time series sequences without extensive manual feature engineering of lag variables. This approach is effective when predicting values based on static features, such as customer demographics in a churn model, rather than capturing the auto-regressive nature required for forecasting.
- ✗
K-Means
Why it's wrong here
K-Means is for clustering, not forecasting.
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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.