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

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

62 practiced

You run a real-time feature store with high-cardinality categorical features used in online scoring. Propose data structures and encoding strategies to minimize memory footprint and lookup latency: consider hashing tricks, compressed embedding tables, tiered storage (in-memory LRU + SSD), cold-key aggregation, and eviction policies. Discuss consistency and replication concerns.

HardTechnical

54 practiced

Explain how you would implement the LAMB optimizer (Layer-wise Adaptive Moments) to enable stable training with extremely large batch sizes. Cover algorithmic details (moment updates, trust ratio scaling), per-layer normalization, weight decay semantics, numerical precision concerns, and engineering optimizations such as tensor fusion and custom kernels to reduce memory traffic.

MediumTechnical

56 practiced

Implement, in Python or clear pseudocode, an efficient routine to compute C = A * S where A is dense (M x D) and S is sparse in CSR format (D x N). The implementation should iterate only over non-zero entries of S, be memory-conscious, and optimized for cache locality. Explain time and space complexity and when sparse multiplication is preferable to dense.

EasyTechnical

52 practiced

Explain and compare the time and space complexity of batch gradient descent, stochastic gradient descent (SGD), and mini-batch SGD when training a model. Assume dataset size N, number of parameters P, batch size B, and E epochs. For each method, describe (1) per-epoch computational cost, (2) memory requirements during training (parameters + batch), and (3) practical trade-offs for convergence speed, variance of updates, and hardware utilization (single GPU vs multi-GPU).

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