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

0 practiced

Explain a practical end-to-end workflow you, as an AI engineer, would follow to profile and diagnose a slow model inference endpoint. Describe how you establish reproducible baselines, collect CPU/GPU/IO metrics, identify bottlenecks (operator-level, data-loading, network), and validate that a fix actually improved p50/p95/p99 latency and throughput.

HardTechnical

0 practiced

Design a streaming minibatch algorithm for training that performs gradient accumulation across microbatches to simulate a larger effective batch size without increasing GPU memory footprint. Provide succinct pseudocode and explain synchronization and allreduce considerations across distributed data-parallel workers.

MediumTechnical

0 practiced

A Python image preprocessing pipeline using PIL is the CPU bottleneck before GPU training. Describe and sketch two approaches to speed it up: (1) replacing Python loops with NumPy vectorized operations, and (2) parallelizing with multiprocessing or thread pools. Explain the trade-offs including memory and GIL concerns.

HardTechnical

0 practiced

Explain trade-offs between RDMA (InfiniBand) and TCP for parameter synchronization in large-scale distributed training. When does RDMA provide clear benefits, and what are the operational and code-level considerations for adopting RDMA in your training stack?

MediumTechnical

0 practiced

GPU memory fragmentation causes OOM when training models on multi-tenant nodes. Outline a set of mitigation strategies, such as allocator tuning, memory pool preallocation, gradient accumulation, and controlled eviction, and discuss the trade-offs for stability and throughput.

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