You work for a large retailer. You want to use ML to forecast future sales leveraging 10 years of historical sales data. The historical data is stored in Cloud Storage in Avro format. You want to…

Question

Options

• ALoad data into BigQuery and use the ARIMA model type on BigQuery ML.
• BConvert the data into CSV format and create a regression model on AutoML Tables.
• CConvert the data into TFRecords and create an RNN model on TensorFlow on AI Platform
• DConvert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI

• A
  68% (17)
• B
  16% (4)
• C
  4% (1)
• D
  12% (3)

Explanation

A is correct because BigQuery ML is designed for fast and rapid experimentation and it is possible to use federated queries to read data directly from Cloud Storage. Moreover, ARIMA is considered one of the best in class for time series forecasting. B is not correct because AutoML Tables is not ideal for fast iteration and rapid experimentation. Even if it does not require data cleanup and hyperparameter tuning, it takes at least one hour to C is not correct because in order to build a custom TensorFlow model, you would still need to do data cleanup and hyperparameter tuning. D is not correct because using AI Platform Training requires preprocessing your data in a particular CSV structure and it is not ideal for fast iteration, as training times can take a long time because it cannot be distributed on multiple machines. https://cloud.google.com/automl-tables/ https://cloud.google.com/automl-tables/docs/features?hl=en#bqml https://towardsdatascience.com/how-to-do-time-series-forecasting-in-bigquery-af9eb6be8159 https://cloud.google.com/bigquery/external-data-sources

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