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.

HardTechnical

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Implement a succinct bitvector supporting rank1(i) and select1(k) in C or C++. Use blocking and precomputed popcounts to achieve good time/space trade-offs. Explain your block layout, auxiliary index structures, and time/space complexity. Discuss how to leverage CPU popcount instructions.

MediumSystem Design

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Design a memory-efficient suffix array-based search system for large text corpora (billions of characters). Describe how you would construct the suffix array (in-memory vs external), store the SA and LCP on disk, and implement fast pattern search with low memory overhead. Discuss trade-offs versus suffix trees and FM-index.

MediumTechnical

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Implement an LRU cache in Python that provides O(1) get(key) and put(key,value) operations and a fixed capacity. Describe how your design ensures O(1) complexity and how you would make it thread-safe for a multi-threaded inference service.

MediumTechnical

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Implement a suffix automaton to compute the number of distinct substrings of a string and support extending the automaton with characters in amortized O(1). Provide the data structure and explain differences vs suffix arrays/trees in memory and operation complexity.

EasyTechnical

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Implement a dynamic array (vector) in C++11 that supports: push_back(T), pop_back(), operator[](size_t), size(), capacity(), clear(), and a shrink_to_fit() method. Your implementation must use doubling growth for amortized O(1) push_back, use move semantics where appropriate, be exception-safe (RAII), and document the time and space trade-offs of your growth policy. Include example usage and edge case handling (zero capacity, reallocation failure).

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