BigQuery Machine Learning: Develop ML Models Where Your Data Liv

For Classification or Regression problems with decision trees, which of the following models is most relevant?

• Linear Regression

• XGBoost

• Wide and Deep NNs

• AutoML Tables

Where labels are not available, for example where customer segmentation is required, which of the following BigQuery supported models is useful?

• Recommendation - Matrix Factorization

• K-Means Clustering

• Time Series Forecasting

• Time Series Anomaly Detection

Which of the following are advantages of BigQuery ML when compared to Python based ML frameworks?

• Moving and formatting large amounts of data takes longer with Python based models compared to model training in BigQuery

• BigQuery ML custom models can be created without the use of multiple tools

• BigQuery ML automates multiple steps in the ML workflow

• All of the options

What are the 3 key steps for creating a Recommendation System with BigQuery ML?

• 
  Prepare training data in BigQuery, train a recommendation system with BigQuery ML, use the predicted recommendations in production

• Prepare training data in BigQuery, select a recommendation system from BigQuery ML, deploy and test the model

• Import training data to BigQuery, train a recommendation system with BigQuery ML, tune the hyperparameters

• Prepare training data in BigQuery, specify the model options in BigQuery ML, export the predictions to Google Analytics

Which of these BigQuery supported classification models is most relevant for predicting binary results, such as True/False?

• DNN Classifier (TensorFlow)

• Logistic Regression

• XGBoost

• AutoML Tables