Quick Answer
The answer is Amazon SageMaker DeepAR, the most appropriate AWS built-in algorithm for time series forecasting with seasonality. DeepAR is specifically designed to handle complex seasonal patterns, such as weekly cycles and yearly trends, by using a recurrent neural network that learns from multiple related time series simultaneously, capturing both seasonality and trend without manual feature engineering. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match SageMaker’s built-in algorithms to specific problem types, often appearing as a scenario where you must distinguish DeepAR from general-purpose models like XGBoost or Linear Learner. A common trap is choosing Linear Learner because it can model trends, but it lacks native support for seasonality, while XGBoost and K-Means are simply not designed for sequential forecasting. Memory tip: think “DeepAR for deep seasonal patterns” — the name itself hints at its strength in capturing recurring cycles in time series data.
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is building a time series forecasting model for daily sales data. The data exhibits strong seasonality with a weekly pattern and a yearly trend. The scientist wants to use Amazon SageMaker's built-in algorithm. Which algorithm is most appropriate?
Amazon SageMaker DeepAR
DeepAR is a built-in algorithm for time series forecasting that handles seasonality and trends.
Linear Learner
Why wrong: Linear Learner can model trends but not seasonality without manual feature engineering.
K-Means
Why wrong: K-Means is for clustering, not forecasting.
XGBoost
Why wrong: XGBoost does not natively handle time series seasonality; requires manual feature engineering.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker DeepAR
DeepAR is designed for time series forecasting with seasonality and trend. Option A is wrong because XGBoost is a tree-based model for tabular data, not specialized for time series. Option C is wrong because K-Means is clustering. Option D is wrong because Linear Learner can model trends but not seasonality natively.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon SageMaker DeepAR
Why this is correct
DeepAR is a built-in algorithm for time series forecasting that handles seasonality and trends.
Related concept
Static NAT maps one inside address to one outside address.
Linear Learner
Why it's wrong here
Linear Learner can model trends but not seasonality without manual feature engineering.
K-Means
Why it's wrong here
K-Means is for clustering, not forecasting.
XGBoost
Why it's wrong here
XGBoost does not natively handle time series seasonality; requires manual feature engineering.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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FAQ
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
The correct answer is: Amazon SageMaker DeepAR — DeepAR is designed for time series forecasting with seasonality and trend. Option A is wrong because XGBoost is a tree-based model for tabular data, not specialized for time series. Option C is wrong because K-Means is clustering. Option D is wrong because Linear Learner can model trends but not seasonality natively.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
easyWhy C: Amazon Forecast's DeepAR+ is built for time series with seasonality.
Keep practising
Last reviewed: Jun 20, 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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