Applying site reliability engineering practices

Your SRE team runs a payment API on GKE with a defined SLO of 99.9% availability measured over a 28-day rolling window. Developers push changes frequently, and leadership wants to maximize release velocity without endangering the SLO. During a new rollout, you want an automated mechanism that decides whether to continue promoting a release based on the release's real impact on the service's reliability signals, halting before the error budget is materially consumed. Which approach best balances change velocity and reliability?

Your team owns a payment-processing API with an SLO of 99.9% availability measured over a 28-day rolling window. Over the last three months, the service has consistently exceeded its target, finishing each window at 99.98% and never consuming more than 15% of its allocated error budget. Product management is pushing hard to accelerate feature delivery, while the SRE team has been spending significant effort maintaining an elaborate multi-region failover system built to protect reliability. Following SRE principles, what is the most appropriate recommendation?

Your team runs a payments API with a 99.9% availability SLO measured over a rolling 30-day window. The on-call engineers complain that their current alert—which pages whenever the instantaneous error rate exceeds 0.1%—generates frequent noisy pages for brief, self-recovering blips, yet occasionally fails to warn them about slow, sustained degradations that quietly consume the monthly error budget. You are asked to redesign the alerting to page only when the budget is genuinely at risk. Which approach best meets this goal?

Your team runs a payments API with a 99.9% availability SLO measured over a rolling 28-day window. Halfway through the current window, a single 45-minute outage has already consumed roughly 90% of the error budget for the period. The product team wants to ship a large feature release this week that touches the payment authorization path, while the on-call engineer has flagged three recurring alerts that require manual intervention each week. As the SRE lead, how should you prioritize the team's work for the remainder of the window?

Your team owns a payments API with a quarterly SLO of 99.9% availability. Six weeks into the quarter, a series of unrelated outages has consumed 95% of the error budget. Product management is pushing to ship three new revenue features in the remaining time, but the SRE team is concerned about further reliability degradation. According to SRE principles, what is the most appropriate way to resolve this tension?

Your payments service has an SLO of 99.9% availability measured over a 30-day rolling window. Two weeks into the current window, a series of latency-related outages has already consumed 95% of the monthly error budget. The product team wants to ship three new features this week, while the SRE team argues the service is too fragile. Your organization follows a documented error budget policy that both teams agreed to in advance. According to standard SRE practice, what should happen next?

Your team runs a payments API with a monthly availability SLO of 99.9%, calculated over a 30-day rolling window. Two weeks into the current window, an unplanned outage has already consumed 80% of the month's error budget. The product team wants to ship a large batch of risky new features immediately to hit a marketing deadline. As the SRE lead, and following Google SRE principles for balancing velocity and reliability, what is the most appropriate recommendation?

A payments service reports 99.95% availability against its SLO, and the error budget shows healthy headroom for the quarter. However, the support team is fielding a steady stream of user complaints about failed checkout attempts, and executives are frustrated that the SLI dashboards look green while customers are clearly unhappy. As the SRE reviewing this discrepancy, what is the most appropriate first action?

Your team launched a new internal API six weeks ago with an availability SLO of 99.0% while it stabilized. Since then, the service has consistently delivered 99.7% availability, error budget burn has been minimal, and downstream teams have begun building critical workflows on top of it. Leadership asks how you should adjust reliability targets as the service matures. What is the most appropriate SRE-aligned action?

During a major outage, your payment service is returning 503 errors to 40% of users. Your team identifies that a recent config change is the likely cause, but a full root-cause investigation could take an hour. The on-call engineer wants to keep debugging the live system to fully understand the failure before acting. As the SRE lead, which action best reflects sound incident management practice?

An SRE team supports a payment API with a well-defined availability SLO. Over the past quarter, on-call engineers report severe alert fatigue: they receive roughly 40 pages per week, but fewer than 5 require any human action. The remaining pages either auto-resolve within a minute or point to conditions that do not threaten the SLO. Leadership wants to improve on-call health while protecting reliability. Which action best addresses the root problem?

After a two-hour outage caused by an engineer applying an untested configuration change directly to production, your SRE team conducts a postmortem. During the review, several managers push to identify who made the mistake and include a formal reprimand as an action item. As the DevOps lead responsible for the site reliability practice, what should you advocate to best align the postmortem process with SRE principles and reduce the likelihood of recurrence?

Your SRE team is being asked to take on-call ownership of a new payments service that the development team built and is planning to launch next month. The SRE team wants to ensure the service meets operational standards before accepting pager responsibility. According to Google SRE practices for managing the service lifecycle, which action should the SRE team take FIRST?

Your team runs a customer-facing checkout API. The current SLO uses average request latency, but customers keep complaining about slow checkouts even though the average latency SLO consistently reports as met. As the SRE, you want to define an SLI that better reflects the actual user experience during periods of degraded performance. Which change to the latency SLI is most appropriate?

Your SRE team is defining an SLI for a customer-facing payments API served on Cloud Run behind a global HTTPS load balancer. Product wants an availability SLO of 99.9% measured over a rolling 28-day window. Requests are logged with HTTP status codes and latency in Cloud Monitoring. Which SLI definition best represents availability for this service and aligns with SRE best practices?

An SRE team runs a payments API with a well-defined availability SLO. Recently, on-call engineers are paged frequently for brief blips that recover within a minute and never threaten the error budget, causing alert fatigue. Meanwhile, a slow, sustained burn last quarter went unnoticed until users complained. The team wants to redesign paging so that engineers are only woken up when a problem meaningfully threatens the SLO, while still tracking slower degradations. What should they implement?

Your SRE team receives dozens of pages each week for a payments API. Many pages fire on internal signals such as high CPU utilization, elevated JVM garbage-collection time, and thread-pool saturation, even though users experience no degradation during those events. Leadership wants alerting that reflects actual customer pain and reduces pager fatigue, while still catching genuine reliability problems. What is the most appropriate change to your alerting strategy?

Your team runs a checkout service and inherited an SLO of 99.99% availability. Over the last two quarters the service has consistently violated this target, yet customer support has logged zero user complaints, and business stakeholders confirm no revenue impact. The engineering team spends significant effort chasing every minor error-budget burn to defend the target. As the SRE lead, what is the most appropriate action?

Your SRE team is preparing a new payments API for general availability. The product team has committed to an availability SLO of 99.9% measured over a rolling 28-day window. During load testing, the service handles 2,000 requests per second at target latency, but at 2,600 requests per second, the p99 latency doubles and error rates climb above the SLO threshold. Marketing projects a launch-day peak of 2,400 requests per second, with sustained growth afterward. As part of the service lifecycle and launch-readiness review, what should the team do BEFORE approving GA?

Your team runs a checkout service with a 99.9% monthly availability SLO. The checkout service depends on a downstream payments API that another team operates and that has a published SLO of 99.5% availability. During your reliability review, an engineer proposes that your team can safely commit to a 99.95% SLO because your own code has been very stable this quarter. What is the most accurate reliability principle to apply here?