Sre User Journey Slo Automated Error Budget Policy

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Your enterprise engineering team runs a critical web application hosted on Google Cloud and is implementing Site Reliability Engineering (SRE) practices to balance feature delivery velocity with system reliability. The team currently experiences frequent production regressions due to rapid deployments, while internal dashboards primarily track infrastructure health metrics like CPU and memory utilization.

You need to establish Service Level Indicators (SLIs) and Service Level Objectives (SLOs) that reflect actual user experience and implement an automated error budget policy that halts standard feature rollouts in your deployment pipeline when the error budget is exhausted.

Which strategy should you implement?

Define SLIs based on Cloud Audit Logs administrative operation counts, configure security flaw remediation SLAs in Security Command Center, and require two-person branch merge approvals in version control.

Define SLIs based on user request success ratio and frontend/load balancer latency, establish realistic SLO targets tied to critical user journeys, and configure Cloud Monitoring alerting on error budget burn rate to programmatically pause deployment targets in Cloud Deploy.

Define SLIs using Apigee SpikeArrest and Quota consumption counters, set the SLO target to match internal SLA thresholds, and terminate background pipeline runner VMs when client API quotas are exhausted.

Define SLIs based on VM CPU utilization and memory headroom, set strict 100% uptime SLO targets, and configure Cloud Build triggers to cancel build jobs whenever Compute Engine instance CPU exceeds 80%.

Professional Cloud DevOps Engineer

Sre User Journey Slo Automated Error Budget Policy

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