You manage an ecommerce website that has a diverse range of product. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A. Use the historical sales data to train and create a BigQueryML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table. B. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python. C. Use the historical sales data to train and create a BigQueryML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table. D. Use the historical sales data to train and create a BigQueryML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.