PMLE Architecting Low-Code ML Solutions Practice Question
A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?
⚠ Common exam trap
The PMLE exam often tests the distinction between regression and classification models, leading candidates to mistakenly choose LOGISTIC_REG for regression tasks because of the word 'regression' in its name, but it is actually a classification algorithm.
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
✓
BOOSTED_TREE_REGRESSOR
BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.
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