MLA-C01 ML Model Development Practice Question
A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?
⚠ Common exam trap
MLA-C01 often tests the distinction between general ML algorithms (Linear Learner, XGBoost) and purpose-built time-series algorithms (DeepAR) — candidates pick XGBoost because it can be used for regression, missing the specialized forecasting requirement.
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 is well-suited for predicting future values in a sequence, such as sales data, and can handle multiple related time series. The other algorithms are for classification, regression, or text, not 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.
- ✗
BlazingText
Why it's wrong here
BlazingText performs word embeddings and text classification, not regression over sequential numeric observations, so it cannot model sales trends. It is tempting because it handles ordered text sequences, and would be correct for sentiment analysis, topic tagging or training word vectors on product review data.
- ✗
Linear Learner
Why it's wrong here
Linear Learner fits a linear regression or classification function to independent rows; it has no mechanism for temporal dependence, so lagged sales patterns are lost. It is tempting because it performs regression on numeric data, and would be correct for predicting a continuous target from static tabular features.
- ✗
XGBoost
Why it's wrong here
XGBoost builds decision-tree ensembles from independent feature rows and cannot represent time ordering, so it cannot extrapolate a sales trend. It is tempting because it excels at tabular regression and often beats linear models, and would be correct for predicting churn or demand from static customer attributes.
- ✓
DeepAR
Why this is correct
DeepAR is a supervised recurrent neural network algorithm purpose-built for time-series forecasting, handling multiple related series and probabilistic predictions. It satisfies the stem's historical sales forecasting requirement, unlike classification, regression, or clustering algorithms that ignore temporal ordering.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.