MLA-C01 ML Model Development Practice Question
Which SageMaker built-in algorithm should be used for forecasting time series data with seasonal patterns?
⚠ Common exam trap
MLA-C01 often tests the confusion between algorithms for different tasks, where candidates might choose BlazingText for time series because it sounds like it handles sequences, but it is actually for text.
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 using recurrent neural networks (RNNs). It excels at handling complex seasonal patterns and can incorporate additional features, making it ideal for forecasting time series data with seasonality. It is the only built-in algorithm among the options that is purpose-built for time series 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.
- ✗
IP Insights
Why it's wrong here
IP Insights learns entity-IP interaction patterns for anomaly detection, producing no time-series forecasts or seasonal decomposition. It is tempting because it is a built-in SageMaker algorithm, and it would be correct if the task were flagging suspicious login or IP-usage behaviour rather than forecasting seasonal demand.
- ✗
BlazingText
Why it's wrong here
BlazingText handles text classification and word embeddings for natural language tasks; it has no mechanism for modelling temporal dependencies or seasonal cycles. DeepAR, a recurrent neural network forecasting algorithm, is the built-in choice for time series exhibiting recurring seasonal patterns.
- ✓
DeepAR
Why this is correct
DeepAR is a supervised recurrent neural network algorithm built into SageMaker that learns from multiple related time series and models seasonality and uncertainty, producing probabilistic forecasts, which matches the requirement for forecasting data exhibiting seasonal patterns.
- ✗
Factorization Machines
Why it's wrong here
Factorization Machines perform supervised classification and regression on sparse feature interactions, such as click-through prediction; they model no temporal ordering or seasonality. DeepAR, which learns autoregressive recurrent patterns across related series, is the built-in algorithm designed for seasonal forecasting.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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