MLA-C01 ML Model Development Practice Question
Which SageMaker built-in algorithm is designed for time series forecasting?
⚠ Common exam trap
The trap is confusing general-purpose algorithms like Linear Learner with specialized time series algorithms. Candidates might think any regression algorithm can forecast, but DeepAR is specifically designed for sequential data.
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 a SageMaker built-in algorithm specifically designed for time series forecasting. It uses recurrent neural networks (RNNs) to predict future values based on historical data, making it ideal for forecasting tasks such as demand prediction or sales forecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear Learner
Why it's wrong here
Linear Learner performs regression and classification on tabular data, not sequence forecasting. It is tempting because regression can predict numeric values, so it would be the right choice for forecasting a single target from independent features rather than modelling temporal dependencies across time steps.
- ✗
Factorisation Machines
Why it's wrong here
Factorisation Machines handle sparse high-dimensional data for classification and regression, not temporal sequence modelling. It is tempting because they excel at recommendation-style tasks with categorical interactions, so they would be correct for click-through or rating prediction rather than forecasting values across a time series.
- ✓
DeepAR
Why this is correct
DeepAR is the only SageMaker built-in algorithm purpose-built for time series forecasting, using autoregressive recurrent neural networks to model probability distributions across many related series. It satisfies the stem's forecasting constraint directly, unlike clustering, classification or regression algorithms, and supports both training from scratch and transfer learning on related datasets.
- ✗
BlazingText
Why it's wrong here
BlazingText performs word embedding generation and text classification, not forecasting; it consumes tokenised text and outputs vectors or labels. It is tempting because it is a built-in SageMaker algorithm, but the correct choice for time series is DeepAR, which models sequential numeric data.
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