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.

EasyTechnical

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Describe three simple caching strategies you would try first to reduce tail latency for a read-heavy endpoint. For each, name where you'd put the cache (application, service, edge), what you'd cache, and one failure mode to watch for.

MediumTechnical

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Design a benchmarking experiment to compare two serialization formats (JSON vs Protobuf) for a message bus used by microservices. Include sample size, concurrency, payload shapes, latency and CPU metrics to collect, and how you'd make a decision when results conflict.

HardTechnical

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Provide a stepwise postmortem plan when a performance regression led to a costly cloud bill spike. Include detection, containment, root-cause analysis, cost impact quantification, remediation, and preventative actions to avoid recurrence.

EasyTechnical

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Write pseudocode or describe an algorithm for a fixed-window rate limiter that allows N requests per minute per user. Explain how it behaves under burst traffic and a single-server deployment. Do not provide a full production implementation, focus on correctness and edge cases.

EasyTechnical

0 practiced

You're asked to benchmark a request handler implemented in Java or Python to quantify CPU, memory, and latency at different concurrency levels. Outline a measurement-driven approach including tools you would use, how you'd warm up the system, sample sizes, and how to present results to stakeholders.

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