A financial data enterprise runs an OLTP database on Cloud SQL Enterprise Plus with high availability (HA) enabled in its primary region. To meet regulatory compliance, the architecture requires cross-region disaster recovery (DR) capabilities with minimal impact on production write latency during normal operations, routine zero-data-loss DR drill testing, and automated routing via a DNS write endpoint. Which replication and failover topology should the data engineering team configure?

Designate a cross-region read replica as a DR replica using a cascadable configuration, execute routine drills via switchover operations, and invoke replica failover in an actual regional outage.: What the Solution Is In Cloud SQL Enterprise Plus, advanced disaster recovery relies on designating a cross-region read replica as a designated disaster recovery (DR) replica (configured with the cascadable-replica flag). This architecture uses asynchronous replication across regions to decouple cross-region network latency from production transactions while supporting seamless switchover and failover workflows. How It Meets the Requirements Write latency preservation: Cross-region data replication is fully asynchronous, ensuring that transaction commits on the primary instance are not blocked by inter-region network round-trips. Zero-data-loss DR drills: Cloud SQL supports the switchover operation for drills. During a switchover, the primary instance transitions to read-only mode, transaction logs catch up until replication lag reaches zero, and the DR replica is promoted to primary with zero data loss (RPO = 0). Disaster mitigation: During an actual regional disaster, executing a replica failover immediately promotes the designated DR replica to primary without waiting for the unavailable primary region to respond. Automated traffic routing: Cloud SQL automatically manages a DNS write endpoint that dynamically updates its DNS resolution to point to the newly promoted primary instance across both switchover and replica failover events. Key Advantages and Capabilities Automated topology reversal: After a switchover or when an old primary recovers after failover, Cloud SQL automatically reconfigures the former primary as a replica without requiring manual rebuilds. Consistent network endpoints: Database clients connect via a single stable DNS write endpoint, removing the need for manual connection string reconfigurations across microservices. Cascading topology support: Cascadable cross-region replicas allow provisioning additional local read replicas in the secondary region without multiplying inter-region bandwidth consumption. Why This Is Optimal This configuration balances low write latency during everyday operations with strict RPO compliance during planned maintenance and drill testing, while offering rapid RTO during actual regional failure events.

Cloud Sql Cross Region Dr Switchover Failover Topology

AdvancedProfessional Cloud Data Engineer

An enterprise runs a mission-critical Cloud SQL for PostgreSQL database deployed across multiple Google Cloud regions: Primary instance (db-central): Deployed in us-central1 with High Availability (HA) enabled. Disaster recovery replica (db-west): Cross-region read replica in us-west1. Reporting replica (db-east): Cross-region read replica in us-east1. Application tier: Connects to the database cluster via the Cloud SQL DNS write endpoint. A catastrophic outage renders the entire us-central1 region unavailable. You need to execute an emergency failover to promote db-west to the new primary while minimizing data loss, redirecting application traffic with minimal downtime, and restoring read and HA topology. Which operational procedure should you execute?

Data replication and failover (e.g., Cloud SQL, Redis clusters)

AdvancedProfessional Cloud Data Engineer

An enterprise maintains a primary Cloud SQL for PostgreSQL database in us-central1 and a cross-region read replica in europe-west1 for regional reporting and disaster recovery readiness. During heavy transaction processing periods, operations teams observe growing replication delay. A review of Cloud Monitoring telemetry reveals: Primary and replica CPU utilization levels remain healthy at 55% and 40%, respectively. The database/replication/network_lag metric is elevated near the 25-second reporting threshold. The database/postgres/replication/replica_byte_lag metric with replica_lag_type: sent_location displays a steadily accumulating byte backlog. Which configuration change should you implement on the primary database to reduce network lag and improve cross-region replication throughput?

Data replication and failover (e.g., Cloud SQL, Redis clusters)

AdvancedProfessional Cloud Data Engineer

An enterprise is centralizing sensitive customer transaction data from multiple business units into a shared BigQuery data warehouse. The data engineering and security teams must implement a robust security architecture that satisfies the following requirements: Data Exfiltration Mitigation: Prevent unauthorized movement, copying, or API-based access to the BigQuery datasets from outside the organization or across unapproved projects. Tenant & Regional Isolation: Ensure analysts can only query table rows that match their specific geographic region without creating and managing separate physical tables or authorized views for every team. Field-Level Confidentiality: Automatically mask sensitive columns (such as credit card numbers and credit limits) for standard analysts, while allowing authorized compliance officers with explicit fine-grained permissions to view the cleartext data. Which combination of Google Cloud security mechanisms should the team implement?

Identity and Access Management (e.g., Cloud IAM and organization policies)

AdvancedProfessional Cloud Data Engineer

An enterprise ingestion pipeline processes streaming transactional data that is staged in Cloud Storage before undergoing complex transformations orchestrated by Cloud Composer. Recent schema deviations and malformed payloads have corrupted downstream analytics tables, leading to pipeline halts and unrecoverable data. You need to architect an automated recovery and reprocessing workflow that satisfies the following operational requirements: Automatically quarantine unparseable or corrupted streaming messages without crashing primary pipelines Enable point-in-time recovery to a clean state if corrupted files are written to Cloud Storage Automatically orchestrate reprocessing from validated checkpoints while maintaining security governance and execution auditability Which architecture should you implement?

Preparing for data corruption and missing data

AdvancedProfessional Cloud Data Engineer

Your organization is designing an enterprise data analytics platform in Google Cloud that processes highly regulated healthcare data across Cloud Storage and BigQuery. Your security and compliance policies dictate the following requirements: All data at rest in Cloud Storage buckets and BigQuery datasets must achieve logical data separation and cryptographic isolation using keys owned and controlled by your organization. Cryptographic keys must be stored in FIPS 140-2 Level 3 Hardware Security Modules (Cloud HSM). A strict separation of duties must be enforced so that data engineers managing storage and analytics resources cannot administer or alter key management configurations. All tables created inside analytics datasets and objects written to ingestion buckets must automatically be encrypted without requiring engineers to explicitly supply key identifiers during write operations. Which architecture and configuration strategy should you implement?

Data security (encryption and key management)

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Cloud Sql Cross Region Dr Switchover Failover Topology

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Cloud Sql Cross Region Dr Switchover Failover Topology

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What the Solution Is

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Key Advantages and Capabilities

Why This Is Optimal