The answer is DeepAR, the most appropriate Amazon SageMaker algorithm for this time series forecasting task. DeepAR is purpose-built for forecasting with seasonal patterns and trends, using a recurrent neural network (RNN) to model the conditional distribution of future values based on past observations, and it natively handles multiple time series, missing data, and known seasonal periods like weekly cycles. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match SageMaker built-in algorithms to specific data characteristics—here, the weekly seasonality and slight upward trend in daily sales data are classic signals for DeepAR. A common trap is choosing a regression or classification algorithm, but remember: DeepAR is the only SageMaker algorithm designed explicitly for probabilistic time series forecasting with seasonality. Memory tip: think “DeepAR = Deep Recurrent” for seasonal patterns and trends.
MLS-C01 Modeling Practice Question
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 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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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 can be used with feature engineering (e.g., lag features) but is not purpose-built for time series; DeepAR is more appropriate.
✗
K-Means
Why it's wrong here
K-Means is for clustering, not forecasting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
DeepAR uses a Monte Carlo sampling approach to output probabilistic forecasts (e.g., quantiles) rather than a single point estimate, which is critical for inventory planning under uncertainty. It also supports 'cold-start' forecasting by learning patterns across related time series (e.g., different products) via a shared RNN encoder. Under the hood, the model uses a negative log-likelihood loss (e.g., Gaussian or negative binomial) to learn the distribution parameters at each time step.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
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 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?
medium
A.K-Means
B.Linear Learner
✓ C.DeepAR
D.XGBoost
Why C: 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.
Last reviewed: Jun 24, 2026
Question Discussion
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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.
Question Discussion
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