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.

HardTechnical

68 practiced

You're evaluating whether to add horizontal autoscaling for application servers. What load tests and capacity calculations would you perform to determine the proper autoscaling thresholds, cooldowns, and instance sizes? Include considerations for downstream components.

MediumTechnical

58 practiced

You observe a database replica lagging behind primary during peak writes, causing stale reads. Present a set of mitigations (short-term and long-term) and how you'd test each with minimal user impact.

HardTechnical

58 practiced

As part of a performance review, you're asked to lower 95th percentile latency by 30% across a set of services. Describe how you would break this large objective into measurable sub-goals, choose experiments, and present progress to leadership over a quarter.

HardSystem Design

56 practiced

Design a canary rollout plan to validate that a new asynchronous processing pipeline reduces upstream latency without introducing data loss. Include traffic split, validation metrics, duration, rollback criteria, and post-canary checks.

HardTechnical

59 practiced

You are asked to reduce tail latency for a search API. List and rank five potential optimizations (e.g., request hedging, caching, query prioritization, circuit breakers, batching) and explain how you would measure the incremental benefit of each.

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