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.

EasyTechnical

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Explain big-O time complexity and why an O(n log n) algorithm like mergesort typically outperforms an O(n^2) algorithm like insertion sort for large inputs. Give an example where constant factors or small input sizes make O(n^2) acceptable and describe how you would validate your choice empirically in production.

MediumTechnical

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Explain how to interpret a flame graph produced by perf or pprof for a CPU-bound backend service. Describe what a wide-hot path indicates versus a tall deep stack, how inlining affects the graph, and how you would prioritize optimization work based on the flame graph findings.

HardTechnical

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Explain how compiler optimizations such as inlining, loop unrolling, and constant propagation can alter program behavior in the presence of undefined behavior. How should backend engineers reason about enabling aggressive optimizations while ensuring correctness?

HardTechnical

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Explain zero-copy techniques for serving large payloads and serializing data such as sendfile, mmap, and scatter/gather I/O. Provide examples of when zero-copy is most beneficial, how it interacts with TLS, and common pitfalls like buffer lifetime and backpressure.

MediumTechnical

0 practiced

A Python web service has several blocking I/O endpoints. Outline how you would migrate a blocking-heavy endpoint to an asyncio-based design (or an async framework). Include code sketch or pseudocode for the migration, discussion of third-party blocking libraries, connection pooling, and the impact on memory and CPU usage.

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