A smart grid utility company is deploying a new real-time meter analytics platform on Google Cloud using Cloud Pub/Sub, Dataflow, and BigQuery. You need to establish a structured testing and validation procedure to verify system throughput, data accuracy, and resilience prior to releasing the architecture into full production. In what chronological order should you execute the following testing and validation steps?
- 1Verify regional service quotas (e.g., Dataflow worker limits and BigQuery insertion quotas) and perform an Infrastructure as Code (IaC) dry-run in an isolated staging environment.
- 2Run synthetic load generators to simulate peak telemetry volume and measure pipeline ingestion latency against target Service Level Indicators (SLIs).
- 3Enable production traffic mirroring (shadow traffic) to stream live telemetry into the staging pipeline and compare output accuracy against the legacy system.
- 4Execute fault-injection testing by simulating zonal outages and network disruptions to validate pipeline recovery times and zero-data-loss guarantees.
- 5Promote the architecture to a canary release in production, routing 5% of live meter traffic while monitoring error budgets before full rollout.
Answer
The correct chronological validation sequence begins with verifying regional resource quotas and IaC staging configuration, followed by synthetic load testing to establish baseline SLIs, streaming mirrored production traffic for data correctness validation, executing fault-injection tests for resiliency compliance, and concluding with a canary rollout monitored via error budgets.
A structured cloud solution validation procedure progresses from static environment and quota verification to isolated synthetic load testing, live shadow traffic comparison, resiliency testing under simulated failure conditions, and finally controlled canary release monitoring.
Step-by-Step Solution
Key Concept
Developing Procedures to Test and Validate Technical Solutions