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Difficulty: HardDefining and Managing SLOs, SLIs, and Error Budgets

A enterprise logistics company operates a real-time dispatch routing microservice on Google Kubernetes Engine (GKE) backed by Cloud Spanner. The service maintains a 30-day rolling availability Service Level Objective (SLO) of 99.9%99.9\%. The development team wants to maximize feature deployment velocity, but the Site Reliability Engineering (SRE) team observes that transient network hiccups cause frequent alert noise, while slow, steady error trends deplete the monthly error budget before human operators are notified. Which TWO operational and alerting strategies should the SRE team implement to manage the error budget effectively and balance velocity with system reliability? (Select TWO.)

  1. Implement multi-window, multi-burn-rate alerting policies in Cloud Monitoring that evaluate short-term and long-term consumption windows simultaneously to trigger pages and ticket alerts based on budget consumption speed.Answer
  2. B
    Configure a single static threshold alert that pages on-call engineers whenever the HTTP 5xx error rate exceeds 0.1% over any 5-minute rolling window.
  3. Establish an automated policy that halts non-emergency deployment pipelines and shifts engineering focus to reliability engineering whenever the remaining error budget drops below a predefined safety threshold.Answer
  4. D
    Set up primary paging alerts on GKE node pool CPU and memory utilization exceeding 85% to detect system degradation before SLO violations happen.

Answer

The correct strategies are implementing multi-window, multi-burn-rate alerting policies in Cloud Monitoring and establishing an automated deployment freeze policy triggered by error budget consumption.
The combination of multi-window multi-burn-rate alerting and automated deployment policy freezes forms the core foundation of GCP SRE error budget management. Multi-window multi-burn-rate alerting measures how quickly the budget is consumed across different time intervals, avoiding false positives from short spikes while accurately catching fast and slow budget burns. Automated deployment freeze policies directly balance deployment velocity with availability by using remaining budget as the quantitative signal for releasing features versus working on stability.

Step-by-Step Solution

1
Evaluate alerting strategy requirements for SRE practices.
Identified that static alerts cause high noise during temporary spikes and fail to measure total budget consumption rates over time.
Google SRE best practices recommend multi-window, multi-burn-rate alerts (such as evaluating 1-hour 14x burn rate for immediate pages and 6-hour 6x burn rate for ticket creation) to balance alert precision and recall.
2
Evaluate governance strategies for error budget management and deployment velocity balance.
Identified that error budget policies provide a formal mechanism to control deployment velocity based on current reliability reserves.
When error budget reserves fall below critical thresholds, halting feature releases enforces reliability work until the budget recovers.

Key Concept

Error Budget Management and Multi-Window Burn-Rate Alerting
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Topics

Defining and Managing SLOs, SLIs, and Error Budgets

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