MLA-C01 ML Model Development Practice Question

Which SageMaker built-in algorithm is specifically designed for time series forecasting?

⚠ Common exam trap

MLA-C01 often tests the confusion between general-purpose algorithms (XGBoost) and purpose-built forecasting algorithms (DeepAR) — candidates pick XGBoost because it can be adapted for time series, but the question asks for one 'specifically designed' for forecasting.

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 based on recurrent neural networks (RNNs) specifically designed for time series forecasting. It learns from historical time series data and can predict future values, making it the correct choice for forecasting tasks. It supports both univariate and multivariate time series and can incorporate related time series.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Image Classification
Why it's wrong here
Image Classification trains convolutional models to assign labels to images; it has no temporal component for forecasting. SageMaker DeepAR is the built-in algorithm designed for time series forecasting. Image Classification is tempting because it is a well-known built-in algorithm, but its input is image data, not sequential time series.
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BlazingText
Why it's wrong here
BlazingText performs text classification and word embeddings, so it cannot model temporal dependencies or produce forecasts. It is tempting because it is a built-in SageMaker algorithm for NLP tasks, and would be the right pick for sentiment analysis or text tagging, not time series.
✓
DeepAR
Why this is correct
DeepAR is a supervised recurrent neural network algorithm built into Amazon SageMaker specifically for time series forecasting. It learns from many related series, producing probabilistic forecasts with quantiles, which matches the requirement for a purpose-built forecasting algorithm rather than generic regression or classification.
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XGBoost
Why it's wrong here
XGBoost is a supervised gradient-boosted tree algorithm for classification and regression on tabular data, not temporal sequence modelling. SageMaker's purpose-built forecasting algorithm is DeepAR, which learns from historical time series to predict future values across horizons.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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