PMLE Architecting Low-Code ML Solutions Practice Question
A data analyst wants to train a linear regression model to predict house prices using only SQL queries on BigQuery. Which BigQuery ML model type should they use?
⚠ Common exam trap
Google often tests the distinction between regression and classification model types, and the trap here is that candidates might confuse LOGISTIC_REG (classification) with linear regression due to the word 'logistic' sounding similar to 'linear', or they might overcomplicate the solution by choosing a tree or neural network model when a simple linear model suffices.
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_REG
The question specifies a linear regression model for predicting house prices, which is a regression task with a continuous target variable. BigQuery ML's LINEAR_REG model type is explicitly designed for linear regression, making it the correct choice for this use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BOOSTED_TREE_REGRESSOR
Why it's wrong here
BOOSTED_TREE_REGRESSOR fits an ensemble of decision trees, capturing non-linear splits and feature interactions rather than a single linear equation. It suits tabular data where relationships are non-linear. The stem explicitly requests linear regression, which BigQuery ML implements as LINEAR_REG, so tree boosting answers a different modelling question.
- ✗
LOGISTIC_REG
Why it's wrong here
LOGISTIC_REG performs classification, predicting a discrete label such as yes/no, not a continuous numeric value like house price. It would be correct for predicting whether a house sells above asking price. Regression on a continuous target requires LINEAR_REG, making LOGISTIC_REG the wrong model family for this task.
- ✓
LINEAR_REG
Why this is correct
LINEAR_REG is BigQuery ML's built-in linear regression model type, trained directly with CREATE MODEL on a numeric label such as house price. It satisfies the constraint of using only SQL, requiring no exported data or external framework.
- ✗
DNN_REGRESSOR
Why it's wrong here
DNN_REGRESSOR builds a deep neural network, which fits non-linear relationships and needs substantially more data and tuning than the stem's simple linear regression on house prices. It is the right choice when interactions and non-linearity dominate. Linear regression maps directly to LINEAR_REG, so the extra depth is unnecessary here.
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