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

56 practiced

You're migrating an on-prem transactional relational database to a managed cloud offering (RDS). Given current metrics and a projected growth rate, describe how you would estimate required storage capacity, IOPS, and instance class. Include how to test assumptions (benchmarks/load tests), handle peak-to-average multipliers, and avoid both overprovisioning and underprovisioning.

EasyTechnical

46 practiced

List and explain the primary cost drivers in cloud environments (compute, storage, network egress, managed services, I/O, licensing). For each cost driver give one practical optimization technique (short term) and one metric you would monitor to detect cost anomalies or regressions.

MediumTechnical

46 practiced

Implement or describe pseudocode for an in-process batching component that collects incoming requests and flushes them to a downstream API when the batch size reaches N or maximum latency T is exceeded. Include handling of shutdown flushing, retry semantics for failed batches, and a simple backpressure mechanism when the downstream is slow.

EasyTechnical

61 practiced

What is request batching and why is it used in backend systems and cloud services? Give two concrete examples where batching improves throughput and resource efficiency (for example: database writes, RPC fan-outs), and explain two trade-offs introduced by batching such as increased latency or complexity in error handling.

EasyTechnical

55 practiced

Explain asynchronous processing patterns such as background workers, message queues, and event streams. Describe when to prefer asynchronous designs over synchronous ones, and how asynchronous architectures influence capacity planning, operational complexity, and cost profiles.

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