Performance Engineering and Cost Optimization Questions

Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.

HardTechnical

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Create a cost-performance model comparing serverless functions and a managed container cluster for a bursty event-driven workload with 1M events/day, median processing time 200ms, and 99th percentile 2s. List model inputs, assumptions, and the decision thresholds you would use to recommend one platform over the other.

HardSystem Design

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Design a workload-shaping system to gracefully handle a 100x traffic surge from an API while preserving high-value traffic. Include admission control, priority queues, token-bucket limits, backpressure mechanisms, and how you would surface degraded behavior to customers.

MediumTechnical

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For a read-heavy relational database workload, outline optimization strategies including indexing, read replicas, materialized views, partitioning, and denormalization. Provide criteria for choosing among these options.

MediumTechnical

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Design a distributed cache invalidation strategy to minimize stale reads for a multi-region service. Consider TTLs, explicit invalidation, versioned keys, pubsub-based invalidation, and the risk of race conditions or partial invalidation.

HardTechnical

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Given a histogram of request latencies and CPU utilization for the last 90 days, describe an algorithm or step-by-step method to select instance types and autoscaling thresholds that minimize cost while meeting p95 latency SLO. Provide pseudocode or clear decision rules.

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