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Google Cloud Exams
GCP
Associate Data Practitioner
Perform inference using BigQuery ML models
Choose Anomaly Detection Model in BigQuery ML

Diagram

Choose Anomaly Detection Model in BigQuery ML

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A decision tree that branches on labeled data, time-series versus IID rows, and model type to choose ML.PREDICT, AI.DETECT_ANOMALIES, or ML.DETECT_ANOMALIES.

In the Domain

Unsupervised Anomaly Detection

Unsupervised anomaly detection with BigQuery ML identifies outliers without labeled examples of what counts as anomalous. Unsupervised detection is the right choice when the definition of anomalous is uncertain or no labeled training data exists, and it can run against training data or new serving data. For time-series data, the detection function depends on the model type, so the model and function must be matched correctly.

Model	Function	Use when
`TimesFM`	`AI.DETECT_ANOMALIES`	The data is a time series
`ARIMA_PLUS`	`ML.DETECT_ANOMALIES`	The data is a time series
`ARIMA_PLUS_XREG`	`ML.DETECT_ANOMALIES`	The data is a time series with external regressors

Read it in the Domain summary

Domain 5 of 6

Perform inference using BigQuery ML models

Read the Domain summary

Practice this Domain

21 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 5 of 6

Perform inference using BigQuery ML models

Read the Domain summary

Practice this Domain

21 practice questions

Practice this Domain

Practice questions in this domain

1.A data analyst has trained a classification model named customer_churn_model using BigQuery ML to predict customer attrition. The analyst now needs to generate batch predictions on a new dataset of current subscribers stored in a table named active_customers and output the predicted labels alongside customer identifiers for a marketing campaign. Which BigQuery ML function should the analyst use to execute these predictions?
2.A data analyst at a retail company trained a time-series model named sales_data.daily_revenue_model using the ARIMA_PLUS model type in BigQuery ML. The business team needs to project future daily sales for the next 30 days with an 80% prediction interval. Which BigQuery ML SQL query should the analyst run to generate these future values?
3.A data analyst at an e-commerce company trained a linear regression model named sales_models.order_value_predictor using BigQuery ML to estimate the total purchase amount of customer orders. The analyst now has a newly collected test dataset stored in sales_data.recent_orders that includes input features as well as the actual completed order amounts. The analyst needs to assess the predictive accuracy of the model on this new dataset by generating summary performance metrics, including mean absolute error and R² score. Which BigQuery ML statement should the analyst execute?

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Home
Google Cloud Exams
GCP
Associate Data Practitioner
Perform inference using BigQuery ML models
Choose Anomaly Detection Model in BigQuery ML

Diagram

Choose Anomaly Detection Model in BigQuery ML

X LinkedIn Facebook Reddit

In the Domain

Unsupervised Anomaly Detection

Unsupervised anomaly detection with BigQuery ML identifies outliers without labeled examples of what counts as anomalous. Unsupervised detection is the right choice when the definition of anomalous is uncertain or no labeled training data exists, and it can run against training data or new serving data. For time-series data, the detection function depends on the model type, so the model and function must be matched correctly.

Model	Function	Use when
`TimesFM`	`AI.DETECT_ANOMALIES`	The data is a time series
`ARIMA_PLUS`	`ML.DETECT_ANOMALIES`	The data is a time series
`ARIMA_PLUS_XREG`	`ML.DETECT_ANOMALIES`	The data is a time series with external regressors

Read it in the Domain summary

Domain 5 of 6

Perform inference using BigQuery ML models

Read the Domain summary

Practice this Domain

21 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 5 of 6

Perform inference using BigQuery ML models

Read the Domain summary

Practice this Domain

21 practice questions

Practice this Domain

Practice questions in this domain

1.A data analyst has trained a classification model named customer_churn_model using BigQuery ML to predict customer attrition. The analyst now needs to generate batch predictions on a new dataset of current subscribers stored in a table named active_customers and output the predicted labels alongside customer identifiers for a marketing campaign. Which BigQuery ML function should the analyst use to execute these predictions?
2.A data analyst at a retail company trained a time-series model named sales_data.daily_revenue_model using the ARIMA_PLUS model type in BigQuery ML. The business team needs to project future daily sales for the next 30 days with an 80% prediction interval. Which BigQuery ML SQL query should the analyst run to generate these future values?
3.A data analyst at an e-commerce company trained a linear regression model named sales_models.order_value_predictor using BigQuery ML to estimate the total purchase amount of customer orders. The analyst now has a newly collected test dataset stored in sales_data.recent_orders that includes input features as well as the actual completed order amounts. The analyst needs to assess the predictive accuracy of the model on this new dataset by generating summary performance metrics, including mean absolute error and R² score. Which BigQuery ML statement should the analyst execute?