AIF-C01 Fundamentals of AI and ML Practice Question
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?
⚠ Common exam trap
A common mix-up: candidates 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.
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 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.
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 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.
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