A retail analytics team stores three years of daily sales per store in BigQuery and needs a univariate forecast for each of 2,000 stores, refreshed nightly. The data scientists are SQL-fluent but have no Python deployment pipeline, the budget is small, and predictions must land back in BigQuery for dashboards. Which model type and Google Cloud product best fit these constraints?
- ATrain a single deep neural network across all stores using a custom training job, then serve nightly batch predictions through a managed endpoint.
- BBuild per-store ARIMA models with BigQuery ML, scheduling a nightly query that trains and forecasts so results stay in BigQuery. Correct
- CSend each store's history to a large language model with a forecasting prompt, returning the predicted figures into BigQuery each night.
- DUse a managed AutoML tabular pipeline to learn each store's forecast and export the trained models for nightly inference.
Why A is wrong: A DNN can capture sales patterns and a custom job is flexible, but it adds Python and serving overhead the SQL-only team cannot maintain, and a single network ignores per-store univariate structure that ARIMA handles directly.
Why B is correct: ARIMA is the right model for univariate time series, and BigQuery ML lets the SQL-fluent team train and forecast many series in-place at low cost with results already in BigQuery for the dashboards.
Why C is wrong: An LLM feels like a quick no-training shortcut, but it is costly per call at this scale, gives no statistical guarantees for numeric forecasting, and is the wrong tool for structured univariate time series.
Why D is wrong: AutoML can produce strong forecasts without deep coding, but spinning up and exporting models per store is heavier and pricier than an in-database ARIMA query, and it pulls data out of the BigQuery workflow the team wants.