Problem Analysis & Optimization Questions
Core technical skills covering problem analysis, algorithmic thinking, and performance optimization. Includes evaluating time and space complexity, selecting appropriate data structures, designing efficient algorithms, and considering trade-offs to optimize software systems.
MediumTechnical
46 practiced
Your model uses categorical embeddings with a heavy long tail (many infrequent categories). Propose strategies to design embeddings that balance representational capacity and memory: variable-sized embeddings by frequency, hashing trick or shared buckets for rare items, subword/character models for categories with structure, and adaptive embedding tables. Discuss training-time effects and runtime lookup trade-offs.
MediumTechnical
48 practiced
Implement a memory-efficient approach to compute pairwise cosine similarity for N vectors of dimension D. Provide Python pseudocode that uses blocking/chunking and BLAS-backed matrix multiply (GEMM) to compute similarities without materializing the full NxN matrix. Discuss complexity, numerical stability, and GPU offload considerations.
HardSystem Design
53 practiced
For pipelines that perform many joins on massive tables (hundreds of GB), propose algorithmic and engineering optimizations to speed up joins: data partitioning/co-location, broadcast joins for small tables, use of bloom filters to prune rows early, columnar formats (Parquet) with predicate pushdown, and join reordering. Explain trade-offs in memory usage, shuffle/network traffic, and complexity.
MediumTechnical
60 practiced
Design and provide Python pseudocode for an efficient minibatch generator for image training that supports on-the-fly augmentation, shuffling, deterministic reproducibility across epochs, multi-worker prefetching, and bounded memory usage. Discuss trade-offs between threads vs processes, serialization overhead, file formats (JPEG vs TFRecord), and how to debug such a pipeline when it becomes the training bottleneck.
MediumTechnical
52 practiced
You have Python code performing elementwise operations on large NumPy arrays inside Python loops. Demonstrate concrete transformations to optimize it: rewrite using vectorized NumPy broadcasting where possible, show using Numba @njit JIT for loop-heavy logic, and explain when a C/C++ extension (Cython) is warranted. Provide before-and-after pseudocode and expected performance trade-offs.
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