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

Vertex AI

Topic in 1 exam domain

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Vertex AI · Service
vertex-ai · Feature · Generative AI RAG with Cloud SQL template component

Also called: AI Platform (Unified), aiplatform.googleapis.com, Cloud Machine Learning, Google Cloud Vertex AI, Google Vertex AI, VAI, Vertex AI API, Vertex AI on GDC air-gapped appliance, Vertex AI services

Vertex AI is Google Cloud's fully managed machine learning platform used to train, deploy, and host machine learning models and foundation models. On Associate Data Practitioner it comes up as an external machine learning service that you integrate with BigQuery through Cloud resource connections to analyze data using pretrained foundation models.

What you can expect on the exam

Querying foundation models from BigQuery. Expect to integrate BigQuery with Vertex AI via remote connections so SQL queries can invoke pretrained foundation models like Gemini to analyze text data.
Configuring Cloud resource connection authentication. Be ready to configure a Cloud resource connection that generates a system service account, which must be granted IAM permissions to access Vertex AI.
Aligning connection and dataset locations. You may need to ensure that the Cloud resource connection and the BigQuery dataset hosting the remote model share the same location so BigQuery and Vertex AI operate together efficiently.
Registering remote models using DDL. Expect to formulate CREATE MODEL Data Definition Language statements that specify the connection path and model options to interface with pretrained Vertex AI models.

Where this appears on the exam

1 subdomain in 1 of 4 domains

Section 2
Data Analysis and Presentation
1. 2.3Define, train, evaluate, and use ML models
  - Use pretrained Google large language models (LLMs) using remote connection in BigQuery

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

Vertex AI

Topic in 1 exam domain

X LinkedIn Facebook Reddit

Vertex AI · Service
vertex-ai · Feature · Generative AI RAG with Cloud SQL template component

What you can expect on the exam

Querying foundation models from BigQuery. Expect to integrate BigQuery with Vertex AI via remote connections so SQL queries can invoke pretrained foundation models like Gemini to analyze text data.
Configuring Cloud resource connection authentication. Be ready to configure a Cloud resource connection that generates a system service account, which must be granted IAM permissions to access Vertex AI.
Aligning connection and dataset locations. You may need to ensure that the Cloud resource connection and the BigQuery dataset hosting the remote model share the same location so BigQuery and Vertex AI operate together efficiently.
Registering remote models using DDL. Expect to formulate CREATE MODEL Data Definition Language statements that specify the connection path and model options to interface with pretrained Vertex AI models.

Where this appears on the exam

1 subdomain in 1 of 4 domains

Section 2
Data Analysis and Presentation
1. 2.3Define, train, evaluate, and use ML models
  - Use pretrained Google large language models (LLMs) using remote connection in BigQuery

How learners score on this topic

Last updated November 2, 2026

What you can expect on the exam

Where this appears on the exam

Related topics

How learners score on this topic

What you can expect on the exam

Where this appears on the exam

Related topics

How learners score on this topic