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Performance Optimization and Latency Engineering Questions

Covers systematic approaches to measuring and improving system performance and latency at architecture and code levels. Topics include profiling and tracing to find where time is actually spent, forming and testing hypotheses, optimizing critical paths, and validating improvements with measurable metrics. Candidates should be able to distinguish central processing unit bound work from input output bound work, analyze latency versus throughput trade offs, evaluate where caching and content delivery networks help or hurt, recognize database and network constraints, and propose strategies such as query optimization, asynchronous processing patterns, resource pooling, and load balancing. Also includes performance testing methodologies, reasoning about trade offs and risks, and describing end to end optimisation projects and their business impact.

MediumTechnical
57 practiced
A production service is experiencing connection pool exhaustion to a downstream database during peak traffic. Outline a troubleshooting checklist, immediate mitigations to restore availability, and long-term fixes to prevent recurrence including monitoring and capacity planning.
HardTechnical
57 practiced
Explain how NUMA topology and filesystem choice affect latency for memory-intensive server processes. Describe mitigations for NUMA-induced latency such as memory pinning, CPU affinity, huge pages, and appropriate filesystem flags or mount options to minimize page faults and cross-node memory access.
HardTechnical
73 practiced
Implement a simplified lock-free single-producer single-consumer ring buffer in C or C++ to pass small messages between threads with minimal latency. Describe the memory ordering guarantees you rely on, how you avoid false sharing, and how you would test correctness under contention.
MediumTechnical
69 practiced
Discuss serialization formats (JSON, Protobuf, MessagePack, Avro) and their impact on latency and throughput. When should you change serialization format and what trade-offs matter (CPU cost, payload size, schema evolution, developer ergonomics)?
EasyTechnical
52 practiced
How do you determine whether a production process is CPU-bound or I/O-bound? Describe specific tools, commands, and tracing signals you would use (for Linux and common cloud environments), what signals you'd look for, and how you'd present findings to non-engineering stakeholders.

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