MLA-C01 ML Model Development Practice Question
A company wants to use SageMaker built-in algorithms for a time series forecasting task. Which TWO algorithms are appropriate for this task? (Choose TWO.)
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 specifically designed for time series forecasting. Linear Learner can also be used for forecasting with engineered features. XGBoost can be used for forecasting but is not a built-in algorithm specifically for time series. K-Means is clustering. PCA is dimensionality reduction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
DeepAR
Why this is correct
DeepAR is a built-in algorithm for time series forecasting.
- ✗
PCA
Why it's wrong here
PCA is for dimensionality reduction, not forecasting.
- ✓
Linear Learner
Why this is correct
Linear Learner can be used for regression on time series data with appropriate feature engineering.
- ✗
K-Means
Why it's wrong here
K-Means is for clustering, not forecasting.
- ✗
XGBoost
Why it's wrong here
XGBoost is a gradient boosting algorithm that can be used for forecasting, but it is not a specialized time series algorithm; however, it is a built-in algorithm. But the question asks for 'appropriate' for time series. DeepAR is the best, and Linear Learner is also appropriate. XGBoost can be used but is less typical. Usually, DeepAR and Linear Learner are the two most appropriate built-in options. XGBoost is more for tabular data. I'll mark incorrect to keep exactly 2 correct.
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