StudyCerts
StudyCertsProfessional Cloud Data Engineer
professional-cloud-data-engineer
Advanced
Gauge your current knowledge
Advanced
Gauge your current knowledge
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?
Explanations
Related questions
Keep the momentum going with these hand-picked practice scenarios
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 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?
Defining data sources and sinks
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
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 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
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
Want more questions like this?
Get a free certification question every week.