StudyCerts
StudyCertsProfessional Cloud Data Engineer
professional-cloud-data-engineer
Advanced
Gauge your current knowledge
Advanced
Gauge your current knowledge
You are conducting a stakeholder and user-centric analysis for an enterprise migrating its legacy data warehouse to Google Cloud. Through your analysis, you have documented the following requirements and constraints: - **Business Analysts (Downstream Users):** Require immediate performance improvements for their BI dashboards. Their interaction pattern involves heavy concurrent querying. - **Data Engineering Team:** Currently manages complex batch ETL pipelines writing to the legacy warehouse. They lack the capacity to rewrite these upstream pipelines for at least six months. - **Business Leadership:** Requires measurable success criteria showing user adoption of BigQuery within the next 30 days. How should you design the initial phase of the migration to satisfy these stakeholder requirements?
Explanations
Related questions
Keep the momentum going with these hand-picked practice scenarios
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 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 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 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
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
Want more questions like this?
Get a free certification question every week.