Comparing the managed backup and restore options across services
Google Cloud provides native, managed backup and recovery capabilities for its major databases and storage services, reducing operational burden compared to self-managed backup scripts. The right choice depends on RPO/RTO needs, service type, and whether you need point-in-time recovery, cross-region protection, or long-term retention. Understanding each service's built-in mechanism helps you compare trade-offs for the exam.
Must-know
- Cloud SQL offers automated backups (daily, retained up to 365 days), on-demand backups, and point-in-time recovery (PITR) via transaction log replay, enabling recovery to a specific timestamp.
- BigQuery automatically retains a 7-day time travel window (configurable 2-7 days) letting you query or restore table data to a prior state without explicit backup jobs, plus optional table snapshots for longer retention.
- Cloud Storage protects data via Object Versioning (retains prior object versions on overwrite/delete) and can use Bucket Lock/retention policies for immutability, not a traditional backup job.
- Spanner supports backups (full, retained up to 1 year) and PITR via a configurable
version_retention_period(up to 7 days), useful for protecting against accidental writes or deletes. - Backup and DR Service is Google's centralized, policy-driven managed backup solution supporting multiple workloads (Compute Engine VMs, Cloud SQL, and more) with centralized management, unlike per-service native backups.
- Persistent Disks use Snapshots (incremental, stored in Cloud Storage) for point-in-time backup/restore of VM disks, and Snapshot Schedules automate recurring snapshot creation and retention.
A team runs a production Cloud SQL for PostgreSQL instance. After a bad application deploy overwrote several rows, the team needs to restore the database to its exact state at 2:14 PM yesterday, a moment between two scheduled backups. Which Google-managed capability should they rely on?
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.