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

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.

EasyTechnical

0 practiced

Compare three caching strategies for a product detail endpoint: in-process local cache, distributed cache (e.g., Redis), and CDN edge caching for static assets. For each strategy list benefits, common failure modes, invalidation techniques, and how the strategy affects consistency, throughput, and cost for a high-traffic API.

EasyTechnical

0 practiced

Explain database connection pooling: what problem it solves, how pool size affects latency and throughput, and which indicators (for example, connection acquisition time, queue time, and 'max connections reached' errors) show a misconfigured pool. Provide a simple guideline or formula to estimate initial pool size given expected concurrency and number of application instances.

EasyTechnical

0 practiced

A new deployment coincides with a sudden spike in 500 errors and increased response latency. Describe a step-by-step triage plan you would execute in the first 30 minutes to isolate whether the issue is code, configuration, infrastructure, or data. Include specific checks (logs, traces, dashboards), safe low-risk experiments you would run in production, and what evidence would cause you to rollback versus continue investigating.

HardSystem Design

0 practiced

Design an asynchronous write architecture to reduce front-end latency while providing durability guarantees. Describe queue semantics you would choose (at-least-once vs exactly-once), persistence and replication model for the queue, retry/backoff strategies, ordering constraints, monitoring for consumer lag, and how to ensure data is not lost during consumer restarts or partial failures.

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