An enterprise logistics company runs a critical real-time inventory reservation service deployed on Google Kubernetes Engine (GKE) backed by Cloud Spanner. The service processes high-throughput synchronous HTTP requests from mobile applications. To balance feature velocity with operational stability, the Site Reliability Engineering (SRE) team needs to define metrics and alerting mechanisms. Which TWO of the following practices should the team implement? (Select TWO)
- Define the Service Level Indicator (SLI) as the ratio of successful HTTP requests (non- status codes) to total valid requests, evaluated over a rolling 30-day window.Cevap
- Implement multi-window, multi-burn-rate alerts based on error budget consumption rates rather than static metric thresholds.Cevap
- CConfigure high-priority pager alerts triggered immediately whenever backend GKE cluster CPU utilization exceeds for more than 5 minutes.
- DSet the Service Level Indicator (SLI) to a fixed target of and define the measured real-time request success rate as the Service Level Objective (SLO).
Cevap
The team should define the SLI as the ratio of successful HTTP requests to total valid requests over a rolling 30-day window, and implement multi-window, multi-burn-rate alerts based on error budget consumption rates.
Defining the SLI as the ratio of successful requests to total valid requests accurately measures customer-facing reliability. Pairwise, implementing multi-window, multi-burn-rate alerting monitors how quickly the service consumes its error budget, allowing teams to catch both fast outages and slow burn rate trends without generating false-positive alert fatigue.
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Defining SLIs as ratio metrics of user-impacting events and configuring error budget burn-rate alerts.
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