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.

HardSystem Design

0 practiced

Design a hybrid edge+cloud architecture to minimize cross-region egress and latency for personalized recommendations. Requirements: 20M monthly users, 5ms median latency target locally, daily model updates, and cost sensitivity for egress. Explain where to keep models, what to cache, and how to push updates.

MediumSystem Design

0 practiced

Design an architecture to minimize cross-region egress for a multi-region AI API. Requirements: 50M monthly requests globally, low-latency reads, model updates daily, and target egress reduction of 60% vs naive central origin. Consider CDNs, regional caching, model replication, and consistency trade-offs.

EasyTechnical

0 practiced

You discover a runaway training job consuming all cluster GPUs and spiking costs. Outline a concise runbook (step-by-step) you would follow to safely stop costs, preserve reproducibility data (logs, checkpoints), and prevent immediate recurrence.

EasyTechnical

0 practiced

Given the following simplified schema, write a SQL query to compute monthly cost per active customer:

transactions(transaction_id, customer_id, service, cost_usd, occurred_at timestamp)

Requirements: compute total cost and cost per customer for month 2025-06, exclude zero-cost test transactions, and order by highest cost per customer. Use standard SQL.

MediumTechnical

0 practiced

Compare pruning, quantization, and distillation as model compression techniques for reducing inference cost. For each technique list expected compression range, recommended use-cases, and one operational challenge when applying it at scale.

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