Calling a hosted Google language model straight from BigQuery
BigQuery lets you call Google's pretrained large language models directly from SQL by creating a remote model object that points to a Vertex AI endpoint through a BigQuery Cloud resource connection. This enables generative AI tasks like text generation and embeddings without moving data out of BigQuery or standing up separate ML infrastructure.
Must-know
- You must first create a BigQuery Cloud resource connection (CREATE CONNECTION) which provisions a service account used to authenticate to Vertex AI.
- The connection's service account needs the Vertex AI User IAM role (or equivalent permissions) granted before the remote model will work.
- A remote model is created with CREATE MODEL ... REMOTE WITH CONNECTION, specifying the connection and the target pretrained endpoint (e.g., a Gemini model).
- Once the remote model exists,
ML.GENERATE_TEXTis used to generate text output andML.GENERATE_EMBEDDINGis used to produce vector embeddings from a table's text column, both via standard SQL. - The BigQuery dataset, the connection, and the Vertex AI endpoint region must be compatible (often same region) or model creation/inference will fail.
- Calls to these remote LLM functions incur Vertex AI prediction charges in addition to normal BigQuery query costs, so usage should be monitored.
A data analyst at a retail company wants to use a pretrained Gemini model directly from BigQuery SQL to summarize thousands of customer review rows stored in a table, without exporting any data or training a custom model. Which sequence of steps correctly enables this in BigQuery?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
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.