PMLE Architecting Low-Code ML Solutions Practice Question
A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?
⚠ Common exam trap
Google often tests the misconception that any regression model (like BOOSTED_TREE_REGRESSOR) can be naively applied to time-series data, ignoring the need for specialized models that handle temporal dependencies and seasonality natively.
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
✓
ARIMA_PLUS
ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting in BigQuery ML, handling seasonal patterns, trend decomposition, and automatic hyperparameter tuning. It models autoregressive (AR) and moving average (MA) components with seasonal differencing, making it ideal for historical sales data with strong seasonal cycles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MATRIX_FACTORIZATION
Why it's wrong here
MATRIX_FACTORIZATION builds recommendation embeddings from user-item interaction matrices; it cannot model temporal ordering or seasonality in sales history. ARIMA_PLUS is the BigQuery ML type designed for time-series forecasting with seasonal decomposition. MATRIX_FACTORIZATION would be correct for product recommendation, not demand forecasting.
- ✗
BOOSTED_TREE_REGRESSOR
Why it's wrong here
BOOSTED_TREE_REGRESSOR treats each row independently, so it cannot capture autocorrelation or the seasonal cycle across time steps without manual lag engineering. ARIMA_PLUS models seasonality natively in BigQuery ML. BOOSTED_TREE_REGRESSOR suits tabular regression on independent rows, not sequential demand forecasting.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS handles seasonality natively through automatic seasonal decomposition and multiple seasonal period detection, satisfying the strong seasonal patterns constraint in the historical sales data. It also supports forecasting horizons directly in BigQuery ML, letting the data scientist train and predict demand without exporting data.
- ✗
K_MEANS
Why it's wrong here
K_MEANS performs unsupervised clustering of rows into groups; it produces no predictive target and cannot extrapolate future demand from historical sequences. ARIMA_PLUS handles trend and seasonal components in time-series data. K_MEANS would be correct for customer segmentation, not forecasting.
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