Working out when a second copy is worth what it costs
Replication creates synchronized copies of data across zones, regions, or instances to improve availability, read performance, and disaster recovery readiness. On Google Cloud, you choose replication when you need to reduce read latency for geographically distributed users, offload read traffic from a primary database, or protect against regional failures, as opposed to backups which are for point-in-time recovery from data loss or corruption.
Must-know
- Cloud SQL read replicas offload read-only queries from the primary instance and can be promoted to a standalone primary during failover, but replication is asynchronous so some lag is possible.
- Cloud Spanner automatically replicates data synchronously across zones or regions depending on the chosen instance configuration (regional, multi-region), providing strong consistency and high availability without manual replica management.
- Replication is not a substitute for backups: replicated data reflects the same errors or deletions as the source almost immediately, so it does not protect against accidental data corruption or deletion the way backups/snapshots do.
- Cloud Storage dual-region and multi-region buckets replicate objects across locations for higher availability and lower latency to distributed users, while single-region buckets do not provide this geographic redundancy.
- Choose replication when the priority is high availability, disaster recovery across regions, or scaling read throughput; choose backup/export strategies when the priority is recoverability from logical data loss or long-term retention.
- Cross-region replication typically increases cost and, for asynchronous methods, introduces some replication lag, so it should be selected based on the required recovery point objective (RPO) and read-scaling needs rather than by default.
A retail company runs a Cloud SQL for MySQL instance that handles both transactional order processing and hourly analytics dashboards. As the dashboard queries have grown more complex, write latency for order processing has increased noticeably. The data team wants to isolate the analytics workload from the production write traffic without redesigning the application. What should they configure?
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.