Performance Optimization and Latency Engineering Questions

Covers systematic approaches to measuring and improving system performance and latency at architecture and code levels. Topics include profiling and tracing to find where time is actually spent, forming and testing hypotheses, optimizing critical paths, and validating improvements with measurable metrics. Candidates should be able to distinguish central processing unit bound work from input output bound work, analyze latency versus throughput trade offs, evaluate where caching and content delivery networks help or hurt, recognize database and network constraints, and propose strategies such as query optimization, asynchronous processing patterns, resource pooling, and load balancing. Also includes performance testing methodologies, reasoning about trade offs and risks, and describing end to end optimisation projects and their business impact.

MediumTechnical

0 practiced

Compare autoscaling strategies for data processing clusters: CPU-based scaling, queue-length-based scaling, Kafka-consumer-lag-based scaling, and custom metric-based scaling. For each, explain pros/cons, recommended thresholds, effects on latency, and how to prevent thrashing.

HardTechnical

0 practiced

A transactional database shows write latency spikes due to fsync on commit. Propose storage-level and DB-level mitigations: disk selection (HDD vs SSD vs NVMe), RAID vs single device, filesystem mount options, group commit, commit frequency, WAL tuning, and quantify expected latency improvements and durability trade-offs.

EasyTechnical

0 practiced

Describe what resource pooling is and why connection pooling reduces latency for databases. List core pool parameters to tune (max_connections, min_idle, idle_timeout, connection-ttl), how to detect pool exhaustion, and practical mitigations if pools are exhausted under load.

MediumTechnical

0 practiced

A batch job issues thousands of API calls to an internal microservice and experiences high tail latency causing retries and job failure. Propose client-side and server-side mitigations (timeouts, retry policies, concurrency limits, bulk APIs, circuit breakers, prioritization) and explain trade-offs for each suggestion.

EasyTechnical

0 practiced

Describe how caching reduces latency in data retrieval and list typical cache layers used in data platforms (in-memory caches, Redis/memcached, CDN). Also explain two scenarios where caching can increase overall latency or cause incorrect results and how you would detect and mitigate those issues.

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