PMLE Architecting Low-Code ML Solutions Practice Question
A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?
⚠ Common exam trap
The trap here is that candidates see 'forecast' and reach for LINEAR_REG because it is a regression model, forgetting that time-series forecasting requires specialized models like ARIMA_PLUS that handle seasonality and autocorrelation.
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 BigQuery ML's purpose-built time-series forecasting model, designed for exactly this scenario: forecasting future values from historical time-ordered data with minimal SQL coding. It automatically handles seasonality, holidays, and trend decomposition, and supports features like forecasting multiple time series at once. LINEAR_REG could technically model time as a feature, but it cannot capture seasonality or autocorrelation, making it unsuitable for demand forecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
K_MEANS
Why it's wrong here
K_MEANS partitions rows into unsupervised clusters and outputs no future numeric value, so it cannot produce a 12-month demand figure. Clustering suits segmentation or anomaly grouping; forecasting requires a supervised model trained on a timestamp column and a target label.
- ✗
MATRIX_FACTORIZATION
Why it's wrong here
MATRIX_FACTORIZATION predicts interactions between users and items for recommendation, not ordered time-series values. It suits sparse rating matrices such as product suggestions; demand forecasting needs a model that consumes timestamped sales and outputs future numeric values, which this cannot do.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS handles time-series forecasting natively in BigQuery ML, requiring only a single CREATE MODEL statement on the historical sales column. It automatically detects seasonality, trends and holidays, satisfying the 12-month horizon and minimal-coding constraint without exporting data or writing Python.
- ✗
LINEAR_REG
Why it's wrong here
LINEAR_REG fits a straight-line relationship between features and a target, so it cannot capture the seasonality and trend in monthly sales. It suits simple continuous prediction from independent features; BigQuery ML's ARIMA_PLUS is designed for time-series forecasting with a timestamp column.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.