AIF-C01 Fundamentals of AI and ML Practice Question
Which THREE are SageMaker built-in algorithms suitable for regression tasks?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between supervised and unsupervised algorithms, and the trap here is that candidates may confuse dimensionality reduction (PCA) or clustering (K-Means) with regression tasks, assuming any algorithm that processes numeric data can perform regression.
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
✓
Linear Learner
Linear Learner is a SageMaker built-in algorithm that supports both regression and classification tasks. For regression, it models the target variable as a linear combination of input features, optimizing for metrics like mean squared error. It is suitable for regression because it directly outputs continuous values.
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 this is correct
Linear Learner supports regression.
- ✗
K-Means
Why it's wrong here
K-Means is clustering, not regression.
- ✗
PCA
Why it's wrong here
PCA is dimensionality reduction.
- ✓
DeepAR
Why this is correct
DeepAR is for time series forecasting, which is a regression task.
- ✓
XGBoost
Why this is correct
XGBoost supports regression.
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