Quick Answer
DeepAR is the correct choice because it is purpose-built for time series forecasting with trend and seasonality, using autoregressive recurrent neural networks to capture complex temporal patterns. Unlike simpler algorithms like Linear Learner or XGBoost, DeepAR excels at learning from multiple related time series simultaneously and generates probabilistic forecasts with confidence intervals, making it ideal for monthly sales data that exhibits both upward trends and seasonal cycles. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to match SageMaker algorithms to specific data characteristics—a common scenario in the Machine Learning Implementation and Operations domain. A frequent trap is choosing a regression algorithm like Linear Learner, which cannot inherently model seasonality or temporal dependencies. Remember the memory tip: DeepAR = Deep Autoregressive, designed for time series with both trend and seasonality, so think “DeepAR dives deep into time patterns.”
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 wants to build a model to forecast monthly sales. The data is a time series with trend and seasonality. Which SageMaker algorithm is most appropriate?
XGBoost
Why wrong: XGBoost can handle some time series, but it does not inherently model temporal dependencies.
K-Means
Why wrong: K-Means is a clustering algorithm, not for forecasting.
Linear Learner
Why wrong: Linear Learner assumes linear regression but does not capture seasonality well.
DeepAR
DeepAR is a built-in SageMaker algorithm specifically for time series forecasting with seasonality and trend.
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 the most appropriate algorithm because it is specifically designed for time series forecasting, handling both trend and seasonality through autoregressive recurrent neural networks. It learns from multiple related time series and produces probabilistic forecasts, making it ideal for monthly sales prediction.
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.
XGBoost
Why it's wrong here
XGBoost can handle some time series, but it does not inherently model temporal dependencies.
K-Means
Why it's wrong here
K-Means is a clustering algorithm, not for forecasting.
Linear Learner
Why it's wrong here
Linear Learner assumes linear regression but does not capture seasonality well.
DeepAR
Why this is correct
DeepAR is a built-in SageMaker algorithm specifically for time series forecasting with seasonality and trend.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often choose XGBoost or Linear Learner because they are familiar with regression tasks, but fail to recognize that time series forecasting requires algorithms that explicitly model temporal dependencies and seasonality, which DeepAR is built for.
Detailed technical explanation
DeepAR uses a recurrent neural network (RNN) with LSTM cells to model the conditional distribution of future time steps given past values and optional covariates. It employs a negative log-likelihood loss function (e.g., Gaussian or Student's t-distribution) to output prediction intervals, which is critical for business planning where uncertainty quantification matters, such as inventory management.
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
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: DeepAR — DeepAR is the most appropriate algorithm because it is specifically designed for time series forecasting, handling both trend and seasonality through autoregressive recurrent neural networks. It learns from multiple related time series and produces probabilistic forecasts, making it ideal for monthly sales prediction.
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 25, 2026
This AIF-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 AIF-C01 exam.
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