PMLE Architecting Low-Code ML Solutions Practice Question
A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?
⚠ Common exam trap
PMLE often tests the confusion between regression and classification model types, leading candidates to pick LINEAR_REG for binary outcomes or K_MEANS for supervised tasks.
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
✓
LOGISTIC_REG
LOGISTIC_REG is the BigQuery ML model type for binary classification, predicting a binary outcome such as churn (yes/no). It uses logistic regression to estimate the probability of the binary outcome. This is the correct choice for predicting customer churn.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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MATRIX_FACTORIZATION
Why it's wrong here
MATRIX_FACTORIZATION builds recommendation models by decomposing a user-item interaction matrix, producing latent factors rather than a churn probability. It is tempting because it also handles customer data, and would be correct for product recommendations, not binary classification.
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LINEAR_REG
Why it's wrong here
LINEAR_REG predicts a continuous numeric value by fitting a straight line, so its output is unbounded and unsuitable for a binary churn label. It is tempting because it is a supervised model, and would be correct for forecasting a numeric quantity such as expected revenue.
- ✓
LOGISTIC_REG
Why this is correct
LOGISTIC_REG performs binary logistic regression inside BigQuery, outputting probabilities for two-class labels such as churn or no churn. It satisfies the stem's requirement for a binary classification model built directly through SQL, with no data export or external tooling needed.
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K_MEANS
Why it's wrong here
K_MEANS performs unsupervised clustering, assigning rows to centroids without a target label, so it cannot predict churn. It is tempting because it groups customers by behaviour for segmentation, and would be correct if the goal were discovering natural customer segments rather than classifying known churn outcomes.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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