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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
0 practiced
Implement, in high-level pseudo-code or algorithm description, a simple client-side rate limiter (token bucket) to avoid overloading a downstream service. Explain how you'd test it under concurrency and how it affects throughput and latency.
HardTechnical
0 practiced
Design a lightweight A/B experiment to measure the impact of a new batching mechanism that batches writes every 100ms. Include how to handle bias from differing workload patterns, metrics to monitor, and how to determine statistical significance in a short window.
HardSystem Design
0 practiced
Design a partitioning/sharding strategy for a user-events table that stores billions of rows and currently causes long tail queries and slow compaction. Give clear partition keys, migration considerations, and how to maintain hot-shard mitigation.
MediumTechnical
0 practiced
Explain how to measure and reason about resource efficiency (requests per CPU-core, memory per 10K requests) for a microservice. Show how to convert these per-request metrics into cluster sizing recommendations.
MediumTechnical
0 practiced
Describe how you would build a reproducible local benchmark harness for a multi-threaded service so that developers can reproduce high CPU scenarios. Include how to mock dependencies, simulate realistic request payloads, and measure per-thread CPU and latency.

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