Answer-first summary for fast verification
Answer: Use BigQuery ML to build a statistical ARIMA_PLUS model.
Option C is the optimal choice because it aligns with the requirement for quick implementation with minimal effort. BigQuery ML's ARIMA_PLUS model is pre-built, handles time series forecasting effectively (including seasonality and trends for monthly sales data), and integrates seamlessly with the existing BigQuery data storage. This eliminates the need for data movement, custom coding, or complex deployment, unlike options A, B, and D, which require additional setup, expertise, or tuning. The community discussion strongly supports C (100% consensus), emphasizing ease of use, minimal effort, and suitability for the data scale and prediction frequency.
Author: LeetQuiz Editorial Team
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You work for an online retailer with several thousand short lifecycle products and five years of sales data in BigQuery. You need to build a model for making monthly sales predictions for each product. The solution should be implemented quickly with minimal effort. What should you do?
A
Use Prophet on Vertex AI Training to build a custom model.
B
Use Vertex AI Forecast to build a NN-based model.
C
Use BigQuery ML to build a statistical ARIMA_PLUS model.
D
Use TensorFlow on Vertex AI Training to build a custom model.
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