- A
XGBoost
Why wrong: XGBoost is not specifically designed for time series forecasting.
- B
K-means clustering
Why wrong: K-means is for clustering, not forecasting.
- C
DeepAR+
DeepAR+ is designed for time series with seasonality and trends.
- D
Linear regression
Why wrong: Linear regression may not capture seasonality well.
Question 1,196 of 1,755
DeepAR+ for Seasonal Time Series Forecasting
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 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?
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 strong seasonality, as it uses recurrent neural networks (RNNs) to capture complex temporal dependencies and automatically models seasonal patterns like yearly cycles. Amazon Forecast natively supports DeepAR+ for such use cases, making it the optimal choice over general-purpose or non-forecasting algorithms.
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.
- ✗
XGBoost
Why it's wrong here
XGBoost is not specifically designed for time series forecasting.
- ✗
K-means clustering
Why it's wrong here
K-means is for clustering, not forecasting.
- ✓
DeepAR+
Why this is correct
DeepAR+ is designed for time series with seasonality and trends.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Linear regression
Why it's wrong here
Linear regression may not capture seasonality well.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose XGBoost (A) because it is a powerful general-purpose algorithm, but they overlook that Amazon Forecast provides specialized algorithms like DeepAR+ for time series tasks, and XGBoost is not a native Forecast algorithm.
Detailed technical explanation
How to think about this question
DeepAR+ uses an autoregressive RNN architecture with a negative binomial or Gaussian likelihood output, allowing it to produce probabilistic forecasts (e.g., prediction intervals) rather than point estimates. It also supports cold-start forecasting by learning from related time series (e.g., multiple product sales), which is critical when historical data is sparse. In practice, DeepAR+ can handle multiple granularities (e.g., hourly, daily) and automatically scales with dataset size via distributed training.
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 strong seasonality, as it uses recurrent neural networks (RNNs) to capture complex temporal dependencies and automatically models seasonal patterns like yearly cycles. Amazon Forecast natively supports DeepAR+ for such use cases, making it the optimal choice over general-purpose or non-forecasting algorithms.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
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 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?
hard- ✓ A.Amazon SageMaker DeepAR
- B.Linear Learner
- C.K-Means
- D.XGBoost
Why A: DeepAR is a built-in SageMaker algorithm specifically designed for time series forecasting, capable of capturing seasonality and trends. It outperforms general-purpose algorithms on such data. Option B (Linear Learner) is wrong because it does not handle seasonality natively. Option C (K-Means) is wrong as it is a clustering algorithm. Option D (XGBoost) is wrong because it is not specialized for time series and does not inherently model temporal dependencies.
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Last reviewed: Jul 4, 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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