Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.
MediumTechnical
45 practiced
You must decide whether to rewrite a Python preprocessing pipeline into C++ to achieve a 5x latency improvement. Outline experiments, benchmarks, and criteria (including performance goals, engineering cost, testability, deployment complexity, and operational risk) to evaluate before committing to a full rewrite. Describe how you would prototype and measure the ROI.
MediumTechnical
62 practiced
You're building similarity search for 10M vectors where low tail latency matters. Compare exact brute-force search vs approximate nearest neighbor approaches (e.g., FAISS IVF, HNSW). Discuss index build time, query latency and tail latency, memory footprint, accuracy trade-offs, support for updates, and operational complexity. What benchmarks and metrics would you collect to choose an approach?
MediumTechnical
45 practiced
Compare post-training static quantization, dynamic quantization, and quantization-aware training (QAT) for reducing model size and inference latency. For each method, explain how it works, typical accuracy impact on NLP and vision models, hardware support, and when you'd apply it. Also describe pruning approaches (magnitude-based, structured) and how pruning and quantization interact.
MediumSystem Design
91 practiced
Design a caching strategy for an embedding retrieval service that handles very high QPS using an ANN index stored on-disk and an in-memory cache for hot results. Define the cache key, TTL policy, eviction strategy, invalidation when embeddings are updated, consistency model, and the trade-offs between freshness, memory cost, and latency.
EasyTechnical
47 practiced
Define profiling in the context of AI model training and inference. List and compare profiling tools and key metrics you would use to find bottlenecks in a Python + PyTorch training job and in GPU inference (e.g., cProfile, line_profiler, PyTorch profiler, NVIDIA Nsight, nvidia-smi). Explain what traces, timeline views, kernel statistics, and counters you would collect and how you would determine whether the bottleneck is CPU, GPU, memory, disk I/O, or network.
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