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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.

HardSystem Design
0 practiced
A service uses synchronous writes to multiple downstream systems per request. Propose an incremental redesign to reduce request latency while preserving data integrity, including fallback and reconciliation strategies for eventual consistency.
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.
MediumTechnical
0 practiced
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.
MediumTechnical
0 practiced
You suspect request queuing in front of workers is causing increased tail latency. How would you instrument the system to measure queue size, wait time, processing time, and end-to-end latency? Describe metrics, tags, and how to correlate them in traces.
HardTechnical
0 practiced
A service's request rate increases tenfold for an hour daily. Propose a combination of architectural and operational changes to handle this bursty workload while controlling cost. Discuss caching, warm pools, pre-warming, and queuing strategies.

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