Professional Cloud DevOps Engineer
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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 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 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 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%.
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 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 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.
Service Level Indicators (SLIs) are quantifiable operational metrics that reflect the real-time quality of service provided to end users, such as request success rate and latency. Service Level Objectives (SLOs) define precise target reliability goals (e.g., 99.9% successful requests over a 30-day rolling window) agreed upon by engineering and business stakeholders. The difference between 100% availability and the SLO target is the error budget, which represents the tolerable threshold of unreliability. An error budget policy formally dictates governance actions—such as freezing new feature rollouts and prioritizing reliability engineering—whenever the budget is depleted or burning at an unsustainable rate.
This approach aligns directly with Google Cloud Well-Architected reliability principles and SRE best practices by shifting observability to the customer perspective and programmatically coupling error budget burn to delivery automation.
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%.
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