Running predictions against a model you already trained
BigQuery ML lets you generate predictions directly inside BigQuery using SQL, without moving data or provisioning serving infrastructure. The core mechanism is the ML.PREDICT function, which applies a trained (or imported) model to a table or query result and returns predictions alongside the original columns.
Must-know
- ML.PREDICT is the standard syntax: SELECT * FROM ML.PREDICT(MODEL
project.dataset.model_name, TABLEproject.dataset.input_table), and it also accepts a subquery instead of a table. - Non-feature (passthrough) columns in the input are automatically preserved in the output alongside the prediction columns, so you don't need to manually rejoin results to source data.
- For classification models, ML.PREDICT returns
predicted_labeland probability struct columns, and you can adjust the decision threshold using the threshold argument. - Time-series
ARIMA_PLUSmodels use ML.FORECAST instead of ML.PREDICT to generate future forecasted values with confidence intervals. ML.EXPLAIN_PREDICTreturns the same predictions as ML.PREDICT plus feature attribution values, useful for explainability without retraining.- Inference works the same way for imported models (TensorFlow, ONNX, XGBoost) and remote models pointing to Vertex AI endpoints, and cost is based on bytes processed by the query, not a separate serving fee.
A data analyst has trained a BigQuery ML logistic regression model named retail.churn_model on historical customer data that includes a churn label column. The analyst now has a new table, retail.active_customers, that does not contain a label column, and wants to generate a churn prediction for each row. Which query should the analyst run?
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.