Keeping trained models catalogued in one place
Vertex AI Model Registry is the central hub for managing your ML models, giving you a single view of all models, their versions, and their deployment status across Google Cloud. It lets you organize model versions under a single model resource, track lineage, and manage aliases to control which version is used in production without changing downstream references.
Must-know
- Model Registry organizes multiple model versions under one logical model resource, so you don't create a brand-new model entry for every retrained iteration.
- Aliases (such as 'default' or 'champion') let you point applications to a specific model version by name, so you can promote a new version or roll back simply by reassigning the alias rather than changing code.
- Models registered can come from AutoML, custom training, or be imported from BigQuery ML, giving a unified registry regardless of how the model was built.
- Each model version retains its own evaluation metrics, metadata, and training pipeline lineage, enabling side-by-side comparison before promoting a version.
- Deploying a model to an endpoint is a separate step from registering it. Registration alone does not make a model servable; you must deploy a specific version/alias to an endpoint for online predictions.
- BigQuery ML models can be registered to Vertai AI Model Registry automatically or via the ALTER MODEL statement, bridging BigQuery-trained models with Vertex AI's deployment and monitoring tools.
A data practitioner registers a new version of a fraud detection model to Vertex AI Model Registry every week after retraining. Several batch prediction pipelines need to always call whichever version is currently approved for production, without being edited every time a new version is approved. What should the practitioner do to organize the model for this purpose?
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.