Scalability Analysis and Bottleneck Identification Questions

Techniques for analyzing existing systems to find and prioritize bottlenecks and to validate scaling hypotheses. Topics include profiling and benchmarking strategies instrumentation and monitoring of latency throughput error rates and resource utilization; identification of common bottlenecks such as database write throughput central processing unit saturation memory pressure disk input output limits and network bandwidth constraints; designing experiments and load tests to reproduce issues and validate mitigations; proposing incremental fixes such as caching partitioning asynchronous processing or connection pooling; and measuring impact with clear metrics and iteration. Interviewers will probe the candidate on moving from observations to root cause and on designing low risk experiments to validate improvements.

MediumTechnical

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Describe how you would use flame graphs to identify CPU hotspots in a multi-threaded backend service. What information does a flame graph show and how do you interpret wide vs deep stacks in terms of optimization opportunities?

MediumTechnical

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A backend service shows high lock contention leading to poor scaling. Explain three strategies to reduce contention at the code and architecture level and the performance trade-offs of each.

HardTechnical

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You are presented with logs showing increasing retry attempts and growing end-to-end latency. Outline a hypothesis tree to move from observation to root cause and describe how to prune hypotheses efficiently with low-cost experiments.

MediumTechnical

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Explain how network bandwidth constraints can manifest at different layers (application, OS, infrastructure). Given 1 Gbps NICs and observed packet drops, list diagnostic commands/tools and the order you would run them to isolate whether the problem is on the host, network, or remote dependency.

MediumTechnical

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You have a distributed job queue with unpredictable latency spikes. Propose an instrumentation plan that captures queue length, processing rate, consumer lag, and per-job execution time. Show how you'd correlate these metrics to find the root cause of spikes.

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