This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
K-Means
Why wrong: K-Means is an unsupervised clustering algorithm, not for forecasting.
Linear Learner
Why wrong: Linear Learner is for regression/classification, not specialized for time series.
DeepAR
DeepAR is a built-in algorithm for time series forecasting that handles trend and seasonality.
XGBoost
Why wrong: XGBoost can be used for time series but requires feature engineering; DeepAR is purpose-built.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
XGBoost
Why it's wrong here
XGBoost can be used for time series but requires feature engineering; DeepAR is purpose-built.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
DeepAR uses an autoregressive RNN architecture that takes past target values and optional covariates (e.g., day-of-week, month) as input, then outputs parameters of a negative binomial or Gaussian distribution for each time step. It handles missing data gracefully and can learn from multiple related time series simultaneously via shared RNN weights, which is particularly useful for retail sales data where product-level series may share similar seasonal patterns. A subtle behavior is that DeepAR automatically scales the time series by the mean of the training data, which can affect performance if the data has extreme outliers.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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