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

For a model-serving service that ships models to edge nodes and has expensive memory footprint and egress, list practical techniques to reduce model size and bandwidth (quantization, pruning, distillation, weight sharing, Delta updates). For each technique, discuss expected compression range, likely impact on latency/accuracy, and how you would evaluate cost-per-inference improvements.

EasyTechnical

0 practiced

Explain Kubernetes Horizontal Pod Autoscaler (HPA) vs Vertical Pod Autoscaler (VPA) vs Cluster Autoscaler. For each, describe how it impacts cost, what workloads it suits, and one operational risk when relying on it for cost optimization.

MediumTechnical

0 practiced

Design an observability pipeline to detect cost anomalies (for example a sudden 30% increase in spend). Outline required data sources (billing export, metrics, logs), aggregation cadence, anomaly detection techniques (thresholds, seasonal baselines, ML), alerting strategy, and safe first-response automated actions.

HardTechnical

0 practiced

You discover a product feature costing $X/month but delivering little measurable user value. How would you prepare and run a conversation with a skeptical product manager to recommend disabling or deprioritizing the feature? Outline the data, experiments, risk mitigation, and communication plan you would bring to the meeting.

MediumTechnical

0 practiced

Compare serverless functions and managed container platforms for workloads with variable load. Focus on cost drivers: invocation counts, memory-time billing, cold-starts and provisioned concurrency, idle container costs, and developer velocity. Provide practical guidance on when to migrate from serverless to containers for cost reasons.

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