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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.

MediumTechnical
0 practiced
Propose a scheduling and checkpointing pattern to maximize use of spot instances for ETL jobs while minimizing interruption risk. Include job classification, checkpoint frequency, retry/backoff policies, and example metrics to decide which jobs are good candidates for spot vs on-demand.
HardTechnical
0 practiced
Given a partition with files of sizes [s1, s2, ..., sn] and costs c_put per API PUT, c_get per GET, and storage cost per GB-month, design a greedy Python heuristic to group files into compaction targets so total expected cost over 1 month (storage + per-file request overhead + compaction cost) is minimized. Provide pseudocode, describe assumptions, and analyze complexity.
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.
HardSystem Design
0 practiced
Design a cost-aware multi-cloud replication strategy across AWS, GCP, and Azure to maintain availability and performance while minimizing egress and storage overhead. Describe replication topology options (active-active, read-replicas, single-writer), deduplication techniques, consistency models, and how to allocate costs back to teams.
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.

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