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Google Cloud Exams
GCP
Associate Data Practitioner
Define, train, evaluate, and use ML models
BigQuery ML: Train, Evaluate, Predict in SQL

Diagram

BigQuery ML: Train, Evaluate, Predict in SQL

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A process flow showing the BigQuery ML lifecycle: a CREATE MODEL query reads training rows from BigQuery tables and stores a trained model, ML.EVALUATE measures quality on held-out test data, and ML.PREDICT scores new data, with results flowing back into BigQuery tables.

In the Domain

Training, Evaluating, and Predicting in BigQuery ML

The BigQuery ML lifecycle turns prepared tables into trusted predictions in three dependent steps. Training data flows from BigQuery tables into the training query, and the trained model is stored in a dataset like a table or view.

Train: Run a CREATE MODEL query that reads training rows and writes a trained model.
Evaluate: Run ML.EVALUATE with the model and held-out test data to measure quality.
Predict: Run ML.PREDICT with the model and new data to generate predictions.

Read it in the Domain summary

Domain 3 of 3

Define, train, evaluate, and use ML models

Read the Domain summary

Practice this Domain

158 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 3 of 3

Define, train, evaluate, and use ML models

Read the Domain summary

Practice this Domain

158 practice questions

Practice this Domain

Practice questions in this domain

1.A retail company stores several terabytes of structured transaction data in BigQuery tables. The data analytics team needs to train and evaluate a binary classification model to predict customer churn. The team wants to implement this solution using their existing standard SQL skills, without exporting or moving the data outside the data warehouse. Which solution should the team use?Identify ML use cases for developing models by using BigQuery ML and AutoML
2.A retail organization stores several terabytes of customer transaction and interaction data directly in BigQuery. The data analytics team needs to build predictive models for customer churn and group customers into distinct segments for targeted marketing campaigns. The team has strong SQL skills but lacks software engineering experience in Python or R, and they want to avoid exporting data to external systems or managing training infrastructure. Which Google Cloud capability should the team use?Identify ML use cases for developing models by using BigQuery ML and AutoML
3.An e-commerce organization wants to build a machine learning solution to categorize customer support tickets. The tickets contain unstructured data, including free-form text descriptions and uploaded product images showing damaged items. The development team has minimal machine learning expertise and wants a solution that automatically handles feature engineering, architecture selection, and model training without requiring custom deep learning code. Which Google Cloud approach should the organization use?Identify ML use cases for developing models by using BigQuery ML and AutoML
4.A data analyst has a BigQuery table containing thousands of unstructured customer support feedback records. The analyst needs to summarize the text and classify customer sentiment directly within BigQuery using a pretrained Google large language model (LLM), such as Gemini, without moving the data outside the data warehouse. Which approach should the analyst use to perform this text analysis?Use pretrained Google large language models (LLMs) using remote connection in BigQuery
5.A data team is expanding their data warehouse capabilities by integrating external machine learning capabilities into BigQuery. They have set up a BigQuery Cloud resource connection named my_connection in the us multi-region and granted the associated service account the required Vertex AI permissions. Which Data Definition Language (DDL) statement should the team run in BigQuery to register a remote model that connects through this external connection?Use pretrained Google large language models (LLMs) using remote connection in BigQuery

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Home
Google Cloud Exams
GCP
Associate Data Practitioner
Define, train, evaluate, and use ML models
BigQuery ML: Train, Evaluate, Predict in SQL

Diagram

BigQuery ML: Train, Evaluate, Predict in SQL

X LinkedIn Facebook Reddit

In the Domain

Training, Evaluating, and Predicting in BigQuery ML

The BigQuery ML lifecycle turns prepared tables into trusted predictions in three dependent steps. Training data flows from BigQuery tables into the training query, and the trained model is stored in a dataset like a table or view.

Train: Run a CREATE MODEL query that reads training rows and writes a trained model.
Evaluate: Run ML.EVALUATE with the model and held-out test data to measure quality.
Predict: Run ML.PREDICT with the model and new data to generate predictions.

Read it in the Domain summary

Domain 3 of 3

Define, train, evaluate, and use ML models

Read the Domain summary

Practice this Domain

158 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 3 of 3

Define, train, evaluate, and use ML models

Read the Domain summary

Practice this Domain

158 practice questions

Practice this Domain

Practice questions in this domain

1.A retail company stores several terabytes of structured transaction data in BigQuery tables. The data analytics team needs to train and evaluate a binary classification model to predict customer churn. The team wants to implement this solution using their existing standard SQL skills, without exporting or moving the data outside the data warehouse. Which solution should the team use?Identify ML use cases for developing models by using BigQuery ML and AutoML
2.A retail organization stores several terabytes of customer transaction and interaction data directly in BigQuery. The data analytics team needs to build predictive models for customer churn and group customers into distinct segments for targeted marketing campaigns. The team has strong SQL skills but lacks software engineering experience in Python or R, and they want to avoid exporting data to external systems or managing training infrastructure. Which Google Cloud capability should the team use?Identify ML use cases for developing models by using BigQuery ML and AutoML
3.An e-commerce organization wants to build a machine learning solution to categorize customer support tickets. The tickets contain unstructured data, including free-form text descriptions and uploaded product images showing damaged items. The development team has minimal machine learning expertise and wants a solution that automatically handles feature engineering, architecture selection, and model training without requiring custom deep learning code. Which Google Cloud approach should the organization use?Identify ML use cases for developing models by using BigQuery ML and AutoML
4.A data analyst has a BigQuery table containing thousands of unstructured customer support feedback records. The analyst needs to summarize the text and classify customer sentiment directly within BigQuery using a pretrained Google large language model (LLM), such as Gemini, without moving the data outside the data warehouse. Which approach should the analyst use to perform this text analysis?Use pretrained Google large language models (LLMs) using remote connection in BigQuery
5.A data team is expanding their data warehouse capabilities by integrating external machine learning capabilities into BigQuery. They have set up a BigQuery Cloud resource connection named my_connection in the us multi-region and granted the associated service account the required Vertex AI permissions. Which Data Definition Language (DDL) statement should the team run in BigQuery to register a remote model that connects through this external connection?Use pretrained Google large language models (LLMs) using remote connection in BigQuery