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.

HardTechnical

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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.

HardSystem Design

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Design an observability strategy for latency engineering across polyglot microservices. Include choices for distributed tracing, sampling strategies (head vs tail sampling), span enrichment, correlating traces with metrics and logs, storage/retention, query performance, and balancing observability detail vs cost.

HardTechnical

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A product manager wants both strong consistency and global low-latency reads. Describe the fundamental trade-offs (CAP and latency), patterns that partially satisfy both (session routing, read-after-write routing, hybrid approaches), and operational complexity introduced by each pattern.

HardTechnical

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A multi-tenant JVM host shows one tenant causing CPU spikes that increase latency for others (noisy neighbor). Propose detection methods, immediate mitigations to restore fairness, and long-term architectural fixes such as resource isolation, scheduling, cgroups, or different tenancy models.

MediumTechnical

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Compare sampling profilers and instrumentation/tracing for performance analysis in production. Explain overhead trade-offs, granularity differences, accuracy implications, and scenarios where you would prefer sampling over instrumentation or vice versa.

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