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StudyCertsProfessional Cloud Data Engineer
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Advanced
Gauge your current knowledge
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?
Explanations
Related questions
Keep the momentum going with these hand-picked practice scenarios
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
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 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
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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