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Learning From Failure and Continuous Improvement Questions

This topic focuses on how candidates reflect on mistakes, failed experiments, and suboptimal outcomes and convert those experiences into durable learning and process improvement. Interviewers evaluate ability to describe what went wrong, perform root cause analysis, execute immediate remediation and course correction, run blameless postmortems or retrospectives, and implement systemic changes such as new guardrails, tests, or documentation. The scope includes individual growth habits and team level practices for institutionalizing lessons, measuring the impact of changes, promoting psychological safety for experimentation, and mentoring others to apply learned improvements. Candidates should demonstrate humility, data driven diagnosis, iterative experimentation, and examples showing how failure led to measurable better outcomes at project or organizational scale.

HardTechnical
0 practiced
You are asked to reduce MTTR by 50% across the organization in 6 months. Propose a measurable program with initiatives (instrumentation, runbook improvements, playbooks, on-call rotations, drills), KPIs and targets, owners, rollout plan, and methods to validate and iterate on effectiveness.
HardTechnical
0 practiced
You're incident commander during a multi-region outage that risks split-brain in replicated databases. You must choose between immediate failover (improving availability but risking divergence) or keeping degraded service (protecting data). Explain your decision framework, who you consult, how you communicate the decision, and steps to reconcile data post-incident.
MediumTechnical
0 practiced
In Python, implement compute_burn_rate(timeseries, window_minutes, slo_error_budget) where timeseries is a list of (timestamp, is_error) ordered by time. The function should compute burn rate over rolling windows and return windows where burn_rate > 1. Explain handling of missing data and algorithmic complexity.
HardTechnical
0 practiced
A shared library bug is causing incidents across multiple teams, but teams resist upgrading due to compatibility risk. As a staff engineer, propose a remediation plan that includes hotfixes, compatibility shims, automated tests, migration support, rollout coordination, and incentives to accelerate upgrades across teams.
MediumTechnical
0 practiced
You need to add instrumentation to measure error-budget consumption per service. Describe which metrics each service should emit (and their names), label/tag strategy (service, cluster, region), aggregation windows, how to compute burn rate, and how to present alerts and dashboards to SREs and product owners.

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