Advanced Data Structures and Implementation Questions

Deep and practical expertise in advanced data structures, their implementation details, performance characteristics, and selection for both algorithmic problems and production systems. Topics include arrays and dynamic arrays, strings, linked lists, stacks and queues, hash tables, heaps and priority queues, various tree forms including binary search trees and balanced trees, tries or prefix trees, segment trees and binary indexed trees or fenwick trees, union find or disjoint set union, suffix arrays, and advanced graph representations. Candidates should be able to implement core structures from first principles, demonstrate interfaces and invariants, reason about insertion deletion search traversal and iteration costs including worst case average case and amortized analysis, and discuss memory management and ownership in low level languages such as C and C plus plus as well as safe memory and reference use in managed languages. Evaluation also covers trade offs between contiguous and pointer based layouts, cache friendliness, concurrency considerations, selection of structures based on access patterns update frequency and memory constraints, handling of edge cases, testing and performance tuning for realistic inputs, and applying structures to problems such as top K queries prefix search connectivity range queries caches and union operations.

MediumTechnical

83 practiced

Compare representations for graphs used in Graph Neural Network (GNN) training: adjacency list with pointers, CSR (compressed sparse row), and COO. For large-scale multi-GPU training, which representation do you choose and why? Discuss memory layout, GPU coalesced memory access, neighborhood sampling performance, and update costs.

HardTechnical

82 practiced

Implement a dynamic CSR (Compressed Sparse Row) graph representation that supports efficient neighbor iteration and semi-dynamic updates (batched edge insertions/deletions) while avoiding full rebuilds on every update. Describe chunked adjacency arrays, amortized reallocation strategy, and how you maintain per-node degree and offset bookkeeping.

HardSystem Design

97 practiced

You must design data structures to support neighborhood queries and incremental edge updates for large sparse graphs used in GNN training across multiple GPUs. Describe partitioning (edge-cut vs vertex-cut), storage formats (CSR/CSC/COO), halo exchange strategies, and how to maintain consistency and efficiency when edges are added/removed during training.

HardTechnical

69 practiced

For batched sparse tensor-matrix multiplies on GPU (used in sparse attention or embedding layers), compare COO, CSR, and block-sparse formats. For a workload with structured sparsity (block sparsity), design the data structure and GPU kernel access pattern to maximize throughput and minimize memory transfers. Provide reasoning for format selection and a pseudocode sketch of batched multiplication.

MediumSystem Design

70 practiced

Design a memory-efficient compressed (radix) trie for a large vocabulary used in autocomplete: describe node/edge layout, how you store edge labels, how to perform insert/search, and how to support storing frequency counts for top suggestions. Estimate memory per stored word given reasonable assumptions (average length, alphabet size).

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