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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 experimental A/B testing framework to measure the impact of a cost-saving optimization (for example enabling stronger compression in a pipeline) on both cost metrics and user-facing performance metrics. Specify primary metrics, sample size calculation, duration, guardrail metrics, significance testing approach, and rollback criteria.
EasyTechnical
0 practiced
You have limited engineering capacity and need to prioritize cost optimization work for the next 90 days. Describe a prioritization rubric you would use (considering effort, expected monthly savings, risk, and visibility), and provide examples of tactical quick wins versus strategic long-term projects.
MediumTechnical
0 practiced
Your organization must cut data infrastructure spend by 20% in 6 months. Sketch a prioritized program focused on initiatives the data engineering team can own: quick wins (low effort, high impact), tooling/instrumentation investments, and behavioral or governance changes. Include how you'd measure and report progress.
MediumBehavioral
0 practiced
Tell me about a time when you convinced stakeholders to accept a change that temporarily reduced feature velocity but substantially lowered infrastructure cost. Use the STAR format, include how you quantified expected impact, what objections you faced, and what the final outcome and learnings were.
EasyTechnical
0 practiced
List common sources of network egress charges in cloud data platforms (for example cross-region replication, third-party API calls, sharing datasets with customers, BI dashboard exports) and give five practical mitigations to reduce egress costs. Explain how you would estimate the dollar impact of each mitigation.

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