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

60 practiced

You are designing SLO-based monitoring for a payment service. Describe how you'd define SLOs and SLIs, choose error budget burn rate policies, and implement alerting that balances early warning with noise reduction. Include examples of specific SLOs and corresponding alert thresholds.

EasyTechnical

57 practiced

Explain the differences between profiling and benchmarking. For a new microservice, which would you run first and why? Give concrete examples of tools and the types of problems each technique is best suited to identify.

HardTechnical

50 practiced

Design a test and rollout strategy to ensure that an optimization (e.g., query rewrite, caching) that reduces cost by 25% does not regress user experience for underrepresented traffic patterns. Describe canarying, A/B testing, metrics to monitor, and rollback triggers.

EasyTechnical

85 practiced

Discuss the high-level trade-offs between a real-time streaming architecture and a batch-processing architecture for processing user events. Provide three scenarios where batch processing is preferable and three where real-time is required.

EasyTechnical

45 practiced

Discuss the performance trade-offs between sending many small messages vs batching messages for a backend service. Include effects on latency, throughput, memory, and error handling. Provide a simple guideline for choosing a batch size.

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