Building, fitting, and scoring a model with SQL alone

BigQuery ML lets you create, train, and evaluate machine learning models directly in BigQuery using standard SQL, eliminating the need to export data to a separate ML platform. Models are created with CREATE MODEL statements, trained on data via SELECT queries, and evaluated using built-in functions like ML.EVALUATE. This keeps data, training, and prediction all within BigQuery's serverless environment.

Must-know

• The CREATE MODEL statement defines the model type via OPTIONS (e.g., model_type='linear_reg', 'logistic_reg', 'kmeans', 'boosted_tree_classifier') and specifies the label column for supervised models.
• Training data is supplied via the AS SELECT clause of CREATE MODEL, so any query result (joins, filters, transformations) can become training input.
• ML.EVALUATE returns model-specific metrics (e.g., precision, recall, ROC AUC for classification; RMSE, R² for regression) and can be run against a held-out evaluation set or the training data if none is specified.
• ML.PREDICT is used to generate predictions on new data using a trained model, returning predicted labels/values alongside input features.
• BigQuery ML automatically splits data into training and evaluation sets when no explicit split is provided, but you can control this via options like DATA_SPLIT_METHOD.
• Model training and prediction incur BigQuery processing costs based on bytes processed (or flat-rate slots), and some model types (e.g., DNN, boosted trees, AutoML-backed models) may have different pricing or resource behavior than simple linear/logistic models.

Coverage checked against the published exam guide on Aug 12, 2026.

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.