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Performance and Code Optimization Questions

Covers techniques and decision making for improving application and code performance across levels from algorithm and memory access patterns to frontend bundling and runtime behavior. Candidates should be able to profile and identify bottlenecks, apply low level optimizations such as loop unrolling, function inlining, cache friendly access patterns, reducing branching, and smart memory layouts, and use compiler optimizations effectively. It also includes higher level application and frontend optimizations such as code splitting and lazy loading, tree shaking and dead code elimination, minification and compression, dynamic imports, service worker based caching, prefetching strategies, server side rendering versus client side rendering trade offs, static site generation considerations, and bundler optimization with tools like webpack Vite and Rollup. Emphasize measurement first and avoiding premature optimization, and explain the trade offs between performance gains and added complexity or maintenance burden. At senior levels expect ability to make intentional trade off decisions and justify which optimizations are worth their complexity for a given system and workload.

MediumTechnical
31 practiced
Implement an LRU cache in Go with O(1) get and put operations. Your implementation should be safe for concurrent access by multiple goroutines. Describe the synchronization strategy you use, how you would benchmark the cache under high concurrency, and one optimization to reduce lock contention.
EasyBehavioral
20 practiced
Behavioral: Tell me about a time you convinced a product or engineering team to accept a performance-related tradeoff that increased system complexity but improved SLOs. Use the STAR method: describe the situation, the task, the actions you took to persuade stakeholders, and the measurable results and lessons learned.
EasyTechnical
20 practiced
Compare JSON and Protocol Buffers (protobuf) for inter-service communication in terms of serialization/deserialization speed, payload size, CPU cost, schema evolution, and developer ergonomics. For a high-throughput RPC path between microservices, recommend an approach and justify tradeoffs including backward compatibility, human readability, and operational observability.
HardSystem Design
16 practiced
Design a globally distributed cache layer for read-heavy product metadata that must serve the 95th percentile of reads within 50ms globally, support 100k QPS, and provide eventual consistency within 10 seconds. Sketch components (origin DB, regional caches, CDN, invalidation), replication and warming strategies, and failure modes (regional outage, stale reads).
HardTechnical
17 practiced
At scale, several Java services show long-tail latencies correlated with full GC events. Propose a comprehensive remediation that includes tuning GC flags, migrating to G1/ZGC/Shenandoah, reducing allocation churn, introducing off-heap storage or object pooling, backpressure, and a rollout plan with canaries. How would you measure risk and validate improvements safely?

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