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StudyCertsProfessional Cloud Data Engineer
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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?
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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
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?
Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state
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
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
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
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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