Show you can work with data on Google Cloud, from ingesting and analyzing it with BigQuery and Looker to managing access, and track your readiness for the Associate Data Practitioner exam.

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Google Cloud service

BigQuery ML

Topic in 8 exam domains

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BigQuery ML · Service
BigQuery ML · Feature

Also called: BigQuery Machine Learning, BigQuery ML integration, BQML

BigQuery ML is a capability within BigQuery that enables users to create, train, evaluate, and execute machine learning models directly using standard SQL queries. On Associate Data Practitioner it comes up as an in-database approach to building machine learning models, evaluating their performance, and generating predictions across structured datasets.

What you can expect on the exam

Selecting BigQuery ML for structured data. Be ready to identify when BigQuery ML is the appropriate choice for structured tabular data and existing SQL workflows, as opposed to alternatives designed for unstructured data such as images or text.
Training model types with SQL. Expect to recognize SQL statements used to define and train various model types directly in BigQuery ML, including linear regression, logistic regression, and k-means clustering.
Executing batch inference and forecasting. Be ready to apply BigQuery ML models to new datasets for batch inference, time-series forecasting, and anomaly detection while configuring parameters such as forecast horizons.

Where this appears on the exam

6 subdomains in 1 of 4 domains

Section 2
Data Analysis and Presentation
1. 2.3Define, train, evaluate, and use ML models

Practice questions on this topic

3 questions

Identify ML use cases for developing models by using BigQuery ML and AutoML

Execute SQL to create, train, and evaluate models using BigQuery ML

BigQuery ML Linear Regression

Practice this domain

How learners score on this topic

Last updated November 2, 2026

Associate Data Practitioner

Show you can work with data on Google Cloud, from ingesting and analyzing it with BigQuery and Looker to managing access, and track your readiness for the Associate Data Practitioner exam.

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Google Cloud service

BigQuery ML

Topic in 8 exam domains

X LinkedIn Facebook Reddit

BigQuery ML · Service
BigQuery ML · Feature

Also called: BigQuery Machine Learning, BigQuery ML integration, BQML

What you can expect on the exam

Selecting BigQuery ML for structured data. Be ready to identify when BigQuery ML is the appropriate choice for structured tabular data and existing SQL workflows, as opposed to alternatives designed for unstructured data such as images or text.
Training model types with SQL. Expect to recognize SQL statements used to define and train various model types directly in BigQuery ML, including linear regression, logistic regression, and k-means clustering.
Executing batch inference and forecasting. Be ready to apply BigQuery ML models to new datasets for batch inference, time-series forecasting, and anomaly detection while configuring parameters such as forecast horizons.

Where this appears on the exam

6 subdomains in 1 of 4 domains

Section 2
Data Analysis and Presentation
1. 2.3Define, train, evaluate, and use ML models

Practice questions on this topic

3 questions

Identify ML use cases for developing models by using BigQuery ML and AutoML

Execute SQL to create, train, and evaluate models using BigQuery ML

BigQuery ML Linear Regression

Practice this domain

How learners score on this topic

Last updated November 2, 2026

What you can expect on the exam

Where this appears on the exam

Practice questions on this topic

Identify ML use cases for developing models by using BigQuery ML and AutoML

Execute SQL to create, train, and evaluate models using BigQuery ML

Related topics

How learners score on this topic

What you can expect on the exam

Where this appears on the exam

Practice questions on this topic

Identify ML use cases for developing models by using BigQuery ML and AutoML

Execute SQL to create, train, and evaluate models using BigQuery ML

Related topics

How learners score on this topic