Observability capstone: free-recall review
Free-recall prompts spanning the whole observability track — funnel, three-pillar join, OTel architecture, sampling, burn-rate alerting, and ROI.
Retrieval beats re-reading. For each prompt, reconstruct a full answer from memory across the whole track before you open the model answer — the effort of recall is what fuses the separate units into one mental model.
Reconstruct the chapter’s spine without looking back: the debugging funnel, how the three pillars join, the OTel architecture, the sampling and cost levers, burn-rate alerting, and the ROI arithmetic.
- 01Name the five layers of the debugging funnel in order, and state what each layer's output feeds the next.
- 02How do the three pillars — metrics, logs, traces — plus profiles compose into one navigable surface rather than four silos?
- 03Describe the three-layer OTel architecture and what switching backends costs in it.
- 04Why is tail sampling required in addition to head sampling, and what is the canonical production policy?
- 05How does a multi-window burn-rate alert work, and why is it better than a static threshold on the raw error rate?
- 06State the ROI argument for observability as arithmetic, not a slogan, and what each input is measured from.
If you could reconstruct each answer from memory, you hold the chapter’s spine: the funnel narrows SLO → RED → trace → profile → git blame with each step forced by the last; the three pillars plus profiles compose through the trace-id join; OTel is one SDK, one OTLP format, interchangeable backends; tail sampling keeps 100% of what matters and drops the rest; multi-window burn-rate alerting ties pages to budget consumption; and the spend is justified by measurable avoided-outage arithmetic. Observability is not monitoring — it is the substrate that lets you deploy without fear.
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