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

71 practiced

A microservice in Kubernetes shows steadily increasing memory RSS over days, eventually causing OOM kills. Walk through the tools and steps you would use to identify whether this is a memory leak: which metrics to collect, how to capture heap dumps safely, how to analyze leaks (for example dominator trees), and a safe approach to reproduce and fix the bug in staging.

HardTechnical

75 practiced

Network bandwidth between application servers and the primary database saturates during peak, causing increased database query latencies. Propose short-term mitigations (such as query tuning, compression, caching, read replicas) and long-term architectural changes (write-sharding, co-locating services, data model redesign). For each proposal estimate expected bandwidth reduction, implementation complexity, and validation experiments.

EasyTechnical

76 practiced

Name and briefly describe three profiling tools you would use to locate CPU and memory hotspots for a Linux backend service (for example perf, pprof, jmap/jstack). For each tool state: what it measures, a safe way to collect a sample in production with minimal overhead, and one example of how you would interpret its output to identify an actionable fix.

MediumTechnical

75 practiced

Design a low-risk canary experiment to validate that switching to a pooled DB driver reduces latency under production traffic. Specify canary rollout percentage, metrics to compare (latency percentiles, DB connection counts, error rates), monitoring windows, automatic rollback criteria, and how to control for traffic differences between canary and baseline.

HardSystem Design

64 practiced

Design an observability and alerting architecture to detect emerging bottlenecks across a large microservices ecosystem with hundreds of services. Cover metric collection, logging pipeline, distributed tracing strategy, synthetic monitoring, sampling and retention strategy, alert tiering to prevent fatigue, and automated runbooks for common bottleneck events.

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