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 is a general gradient-boosted tree algorithm that does not natively model temporal dependence or seasonality; it needs manual lag features. It is tempting because it performs well on tabular data, but DeepAR is built for time-series forecasting.
- ✗
K-Means
Why it's wrong here
K-Means partitions unlabelled records into k clusters by distance; it models neither temporal ordering nor seasonal cycles, so it cannot extrapolate future sales values. It is tempting because it is a genuine SageMaker built-in algorithm, but it suits customer segmentation or grouping, not forecasting.
- ✗
Linear Learner
Why it's wrong here
Linear Learner fits a linear regression or classification function to independent rows; it has no mechanism for lagged inputs or seasonal indices, so trend and seasonality go unmodelled. It is tempting as a supervised SageMaker algorithm, but it suits tabular regression or classification, not sequential forecasting.
- ✓
DeepAR
Why this is correct
DeepAR is a supervised recurrent neural network designed for time series forecasting. It learns from many related series and natively models trend, seasonality and uncertainty, directly satisfying the stem's requirement for monthly sales data exhibiting both trend and seasonality.
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