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StudyCertsProfessional Cloud Data Engineer
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Your data engineering team needs SSH access to a Compute Engine VM that orchestrates data ingestion pipelines. The VM is located inside a VPC Service Controls perimeter to prevent data exfiltration. The engineers are working remotely from unmanaged client devices. You need to provide secure SSH access to the VM without exposing it directly to the public internet, while ensuring the access complies with the VPC Service Controls perimeter restrictions. What should you do?
Explanations
Related questions
Keep the momentum going with these hand-picked practice scenarios
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 runs three distinct BigQuery workloads: Data Science, ELT, and Business Intelligence (BI). Currently, all queries use the default on-demand pricing model, resulting in highly variable and unpredictable monthly costs. You need to implement a cost-optimized processing architecture that provides predictable pricing while guaranteeing dedicated compute capacity for each workload. Additionally, to maximize resource utilization, any idle compute capacity from one workload must be automatically available to the others. What should you do?
Minimizing costs per required business need for data
AdvancedProfessional Cloud Data Engineer
Your organization uses a Looker dashboard connected to BigQuery to analyze sales performance. The dashboard displays the current month's data and relies on a query that joins a massive `sales_fact` table with multiple smaller dimension tables. Users are reporting high dashboard latency, and the repeated execution of these complex joins is driving up analytical costs. You need to optimize the data layout and leverage BigQuery BI Engine to minimize query latency and reduce costs. What should you do?
Connecting to tools
AdvancedProfessional Cloud Data Engineer
Your data engineering team is analyzing BigQuery query plans and Cloud Billing reports. They notice that analytics queries against a highly normalized snowflake schema are incurring high costs and slow performance due to massive join operations and full table scans on specific date and region columns. You need to minimize query costs and optimize performance for these analytical workloads. Which optimization strategy should you implement?
Minimizing costs per required business need for data
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
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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