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

Given a pre-trained neural network serving high-volume predictions, describe algorithms and provide a Python/PyTorch pseudocode sketch to apply structured pruning and 8-bit quantization. Explain validation steps to ensure accuracy remains within acceptable bounds and estimate expected reductions in memory footprint and inference cost.

MediumTechnical

0 practiced

Propose an organizational cost governance model for a cloud-first company. Include roles (cost owners, approvers), mandatory tagging policy, chargeback or showback approach, monthly review cadence, cost SLOs or guardrails, and KPIs to measure adoption and savings.

EasyTechnical

0 practiced

As an SRE, explain what 'cost per request' and 'cost per active customer' mean. Describe the key inputs needed to calculate them for a cloud service (for example: monthly bill breakdown, total requests, storage GB-months, compute-hours, egress) and give a simple worked example calculation showing how you'd allocate shared infrastructure costs.

MediumTechnical

0 practiced

Explain telemetry sampling strategies (random, rate-limited, deterministic, tail-based/dynamic) that reduce observability ingestion costs while preserving alerting capability. For each strategy describe impact on debugging, alert fidelity, and how you would select sampling rates for a production service.

MediumTechnical

0 practiced

Design a CDN + edge caching strategy for large media files and static assets to reduce origin egress. Discuss cache-control headers, vendor features (regional POPs, origin shielding, compression at edge), cache invalidation patterns, and how you'd measure ROI per asset type.

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