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Observability

SLO and error budgets: free-recall review

Crux Free-recall prompts across the SLO unit. Answer each from memory first, then reveal the model answer and compare — budget math, burn derivation, MWMBR, composites, policy.
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◷ 14 min

Retrieval beats re-reading. For each prompt, say or write a full answer from memory before you open the model answer — the effort of recall is what makes the SLO arithmetic stick when you are deriving it live in a postmortem.

Goal

Reconstruct the unit’s spine — what makes a good SLI, the budget and burn-rate arithmetic, why MWMBR uses AND, the composite ceiling, and the organisational policy — without looking back at the lessons.

Recall before you leave
  1. 01
    Define SLI, SLO, and error budget, and state the budget and burn-rate formulas with one worked number.
  2. 02
    Derive the 14.4x / 6x / 1x burn-rate thresholds from first principles.
  3. 03
    Why does MWMBR alerting use AND between a long and a short window, and what fails with single-window or OR?
  4. 04
    Explain the composite-SLO ceiling and two architectural levers that raise it.
  5. 05
    What must an error budget policy contain, why do the signatures matter, and how does it differ from an SLA?
  6. 06
    Why do low-traffic services break naive SLO arithmetic, and what are the four standard remedies?
Recap

If you could reconstruct each answer from memory, you hold the unit’s spine: an SLI is a user-facing good/total ratio, the budget is (1 − SLO) × events and burn rate normalises the spend, every MWMBR threshold derives from (budget_fraction × period) / window with AND binding a long and short window, journeys multiply to a 0.999^N ceiling that retries and hedging can raise, the error budget policy needs signatures and exclusions to have teeth, the SLO must sit tighter than the SLA, and low-traffic surfaces need probes, aggregation, longer windows, or count-based alerts plus a NaN guard.

Continue the climb ↑SLO and error budgets: PromQL and rule reading
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