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StudyCertsProfessional Cloud Data Engineer
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You are formulating a target state migration plan to move an on-premises data processing environment to Google Cloud VMware Engine. The environment consists of interconnected data integration applications, large database servers, and several VMs with pending system updates. You need to design a phased migration roadmap that minimizes downtime, avoids multiple power cycles for VMs, and mitigates the risk of resource contention in the target architecture. Which strategy should you incorporate into your migration plan?
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Keep the momentum going with these hand-picked practice scenarios
AdvancedProfessional Cloud Data Engineer
Your organization is planning to migrate a legacy data warehouse to BigQuery. Before migrating, you need to perform a technical inventory of the source system to determine migration complexity and identify optimization opportunities. You have the following requirements for the assessment: - Identify tables and databases that are frequently accessed together in a single query to understand data dependencies. - Discover tables with no recent usage or writes to potentially exclude them from the migration and reduce storage costs. - Identify low-latency queries that could be accelerated using BigQuery BI Engine. Which solution should you implement to achieve these requirements?
Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state
AdvancedProfessional Cloud Data Engineer
You are conducting a stakeholder and user-centric analysis for an enterprise migrating its legacy data warehouse to Google Cloud. Through your analysis, you have documented the following requirements and constraints: - **Business Analysts (Downstream Users):** Require immediate performance improvements for their BI dashboards. Their interaction pattern involves heavy concurrent querying. - **Data Engineering Team:** Currently manages complex batch ETL pipelines writing to the legacy warehouse. They lack the capacity to rewrite these upstream pipelines for at least six months. - **Business Leadership:** Requires measurable success criteria showing user adoption of BigQuery within the next 30 days. How should you design the initial phase of the migration to satisfy these stakeholder requirements?
Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state
AdvancedProfessional Cloud Data Engineer
Your team is designing an event-driven data pipeline using Eventarc to trigger a Cloud Run microservice whenever a new file is uploaded to Cloud Storage. The microservice processes the file and updates a stateful database. Because Eventarc guarantees at-least-once delivery, the microservice occasionally receives duplicate events for the same file upload, causing duplicate database entries. You need to implement a solution to prevent data duplication and ensure the database is only updated once per event. What should you do?
Designing system for fault tolerance and managing restarts
AdvancedProfessional Cloud Data Engineer
A data engineering team is migrating a legacy data ingestion pipeline to Google Cloud Managed Service for Apache Kafka. They need to enforce strict schema validation to prevent malformed data from breaking downstream structured sinks. The team has the following requirements for the new pipeline: * Support for a formal schema definition format natively integrated with the Managed Service for Apache Kafka schema registry. * Ensure that consumer applications configured with updated schemas can still read data produced with older schema versions. * Implement a production-ready schema deployment strategy that prevents unexpected schema changes during runtime. Which architecture and configuration strategy should the team implement?
Defining data sources and sinks
AdvancedProfessional Cloud Data Engineer
Your organization uses a custom on-premises visualization tool to generate business intelligence reports. The tool currently connects to relational databases using standard SQL protocols and does not natively support the BigQuery API. You need to integrate this tool with BigQuery to analyze large datasets. Security policies mandate that the connection must not traverse the public internet, and BigQuery resources must be protected from data exfiltration by restricting access strictly to your hybrid network environment. How should you securely integrate the on-premises tool with BigQuery?
Connecting to tools
FundamentalAssociate Data Practitioner
A company operates a critical Cloud SQL for PostgreSQL production database and needs to implement a robust data protection strategy. They have the following requirements for their backup solution: - **Centralized management** of backups for multiple Cloud SQL instances across different Google Cloud projects. - **Long-term retention** for compliance, requiring backups to be kept for several years. - **Granular scheduling** for automated backups, allowing for more frequent than daily backups. - The ability to perform **point-in-time recovery** to restore the database to any specific moment. - Backups must be protected and recoverable even if the original Cloud SQL instance's project is accidentally deleted. Which Cloud SQL backup option should the company choose to meet these requirements?
Compare backup and recovery solutions offered as Google-managed services
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