I knew the definitions cold but stumbled when they pushed me to give concrete numbers.
Start by defining SLI, SLO, and SLA clearly, then apply them to a concrete web API example like a user profile service. Use availability and latency to illustrate each concept, showing how SLIs are measured, SLOs set targets, and SLAs define consequences.
Pro tip: Emphasize that SLAs are contracts with business consequences, while SLOs are internal targets that should be stricter than SLAs to provide a safety margin. Mention error budgets to show how SLOs drive operational decisions.
Briefly define SLI as a quantitative measure, SLO as a target for an SLI, and SLA as a contract with consequences if SLOs are not met.
Select a familiar API, such as a user profile service, and state its purpose to ground the discussion.
For availability, define SLI as the proportion of successful requests, set an SLO like 99.9% over 30 days, and an SLA like 99.5% with penalties.
For latency, define SLI as the percentage of requests served under a threshold (e.g., 200ms), set an SLO like 95% under 200ms, and an SLA like 90% under 200ms with credits.
Discuss how SLOs should be stricter than SLAs, the role of error budgets, and how these metrics influence design and operations.
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This is where I started to lose the thread a bit.
Start by clarifying the SLO target and the service's criticality, then calculate the error budget as (1 - SLO) * total events. Explain how you'd allocate the budget across different failure modes and track consumption using monitoring tools, with alerts and policies for when the budget is depleted.
Pro tip: Emphasize that error budgets are a tool for balancing reliability and innovation; propose a policy where budget depletion triggers a freeze on non-essential changes, but also allow for exceptions with stakeholder buy-in.
Confirm the SLO target (e.g., 99.9% availability), the measurement window (quarterly), and the service scope. Ensure you understand what counts as a 'good' event and what constitutes an error.
Compute the error budget as (1 - SLO) * total events. For example, a 99.9% SLO over 1 million requests gives a budget of 1,000 errors. Express it in both absolute numbers and percentage.
Decide how to distribute the budget across different failure modes (e.g., latency, errors) or teams. Consider reserving a portion for planned maintenance or experiments.
Set up monitoring to track error budget burn in real-time. Use dashboards and alerts for burn rate thresholds (e.g., 2% budget consumed in 1 hour).
Establish what happens when the budget is exhausted: e.g., freeze releases, prioritize reliability work, or escalate to leadership. Also define exceptions and review processes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the meatiest part and probably where I did best.
Start by framing the problem around SLOs and user impact, then walk through the signal-to-alert pipeline: choose symptoms over causes, define error budgets, and use multi-window burn rates to balance sensitivity and specificity. Finally, explain how aggregation, deduplication, and escalation policies with runbooks reduce noise and ensure actionable alerts.
Pro tip: Emphasize that alert fatigue is often a symptom of poor SLO design—if alerts fire frequently, either the SLO is too strict or the system is unhealthy; use error budgets to drive prioritization and avoid paging on every anomaly.
Establish clear SLOs based on user-facing metrics (e.g., latency, error rate) and compute error budgets to quantify acceptable unreliability. This sets the foundation for alert thresholds that reflect business impact.
Alert on symptoms (e.g., elevated error rate, slow responses) rather than causes (e.g., CPU spikes). Use multi-window burn rates to detect both fast and slow burns of the error budget, reducing false positives.
Group related alerts by service, region, or root cause to avoid flooding on-call with duplicate notifications. Implement deduplication logic to suppress repeated alerts for the same issue.
Define escalation policies that route alerts to the right team based on severity and time. Pair each alert with a runbook that provides clear steps for diagnosis and mitigation.
Regularly review alert effectiveness, adjust thresholds, and retire noisy alerts. Use postmortems to refine SLOs and alerting rules based on incidents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.