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StudyCertsProfessional Cloud Data Engineer
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Advanced
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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?
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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 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 manages a real-time analytics web application deployed on a Managed Instance Group (MIG) behind an external Application Load Balancer. During periods of heavy data ingestion, some virtual machines become temporarily busy and are slow to respond to requests. The current configuration uses a single, aggressive health check for both the load balancer and the MIG's autohealing policy. As a result, the autohealer mistakes these busy instances for failed instances and prematurely recreates them, which reduces overall system availability and causes cascading failures. What should you do to ensure the system gracefully handles temporary load spikes while still automatically recovering from true application failures?
Designing system for fault tolerance and managing restarts
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 redesigning a data ingestion pipeline for a high-traffic e-commerce platform. Currently, the order management service sends data to a downstream inventory processing service using synchronous API calls. When the inventory service experiences an outage or a traffic spike, the order management service blocks while waiting for responses. This leads to timeouts and cascading failures across the platform. You need to implement a fault-tolerant pattern to achieve the following: - Isolate failures so the order management service remains functional during inventory service outages. - Prevent cascading effects across the platform. - Ensure no data is lost and the inventory service can resume processing from the point of failure once it recovers. Which architectural approach should you implement?
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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