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Cost Optimization at Scale Questions

Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.

HardTechnical
0 practiced
Design an observability pipeline that reduces ingestion and storage costs by 10x while preserving at least 95% of alerting and debugging fidelity. Include trace sampling strategy, metric rollups and retention tiers, top-N per service tracing, anomaly detection thresholds, and how dashboards/on-call practices must change.
EasyTechnical
0 practiced
Describe how SLOs and error budgets should influence cost-optimization decisions. Provide examples of situations where you would safely use the error budget to pursue cost reductions and where you would avoid doing so because of unacceptable customer risk.
MediumTechnical
0 practiced
Write a program outline or pseudocode in Python that ingests instance utilization metrics and cloud pricing API responses and recommends instance type right-sizing. The output should include current instance, recommended instance type, expected monthly savings, and a confidence score. Describe how you'd compute confidence.
MediumTechnical
0 practiced
Write a Python function that compresses JSON payloads with gzip and computes potential monthly savings in both egress and storage. Use these sample stats: 1,000,000 requests/day, average JSON size 5KB, gzip reduces size by 60%, storage retention 30 days. Show calculations and discuss scenarios where compression might not be worth the CPU cost.
EasyTechnical
0 practiced
What does 'rightsizing' mean for cloud compute resources? List three concrete signals from monitoring that indicate an instance is overprovisioned, and one plausible signal that could be misleading and why.

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