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

You are considering migrating an in-process per-node cache to a central Redis cluster. Outline expected performance impacts on latency, throughput, and tail behavior; propose a migration plan with instrumentation, a rollback plan, and strategies to mitigate network RTT and serialization overhead. Include failover and replication lag handling during rollout.

HardSystem Design

0 practiced

Architect an observability pipeline to ingest, store, and query high-cardinality metrics and distributed traces from a large fleet while keeping trace lookup p99 under 1s for operational debugging. Discuss ingestion, adaptive sampling, downsampling, index strategy, tiered storage (hot/warm/cold), retention policies and how to manage cost vs fidelity trade-offs.

EasyTechnical

0 practiced

What is a flame graph and how do you use it to find CPU hotspots that affect latency? Explain how stack-sampling produces a flame graph, what wide vs tall blocks indicate, and how you'd combine flame graphs with heap or blocking profiles to form a hypothesis about a latency regression.

MediumTechnical

0 practiced

Propose a connection pooling strategy for service instances connecting to a Postgres cluster. Consider DB connection limits, pooling at the client vs sidecar proxy (PgBouncer), idle timeouts, prepared statement handling, connection churn and the effect on p99 latency under bursty traffic. Provide concrete pool sizing guidance relative to DB max_connections and number of app instances.

HardTechnical

0 practiced

You need to implement SLO governance across multiple product teams to drive latency improvements without unduly blocking feature velocity. Describe the governance policy, measurement tooling, enforcement mechanisms (release gates/guardrails), how to use error budgets to prioritize engineering work, and how to handle teams that repeatedly miss SLOs.

Unlock Full Question Bank

Get access to hundreds of Performance Optimization and Latency Engineering interview questions and detailed answers.

Join thousands of developers preparing for their dream job.