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.

MediumSystem Design

0 practiced

Design a low-cost strategy to reduce object storage bills for a service that stores user-uploaded images. The system serves thumbnails frequently but original images are rarely read. Describe storage tiering, lifecycle rules, caching, and any image-processing trade-offs.

MediumTechnical

0 practiced

Describe how you would use flame graphs and CPU flame charts to find CPU hotspots in a Java service. What kinds of issues are visible in flame graphs and which require other tools (e.g., memory leaks)?

MediumTechnical

0 practiced

You manage an API with strict p99 latency targets. Propose an alerting strategy that balances sensitivity and noise: what to alert on, aggregation windows, severity levels, and how to avoid false positives during deployments or gradual traffic shifts.

HardSystem Design

0 practiced

You need to reduce cost by moving cold data from primary disk to cheaper archival storage but still allow occasional reads with reasonable latency (seconds). Design a retrieval path that minimizes cost while keeping implementation complexity moderate. Include caching, prefetch, and user experience considerations.

EasyTechnical

0 practiced

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.

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