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 suffix array construction using prefix-doubling (O(n log n)) in C++. Given a string s, return the suffix array as indices. Focus on stable sorting by rank pairs, memory-efficient rank arrays, and explain how you would adapt this for large alphabets or integer arrays.

EasyTechnical

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Implement a queue using two stacks in Python. Provide enqueue(x) and dequeue() methods and show why the amortized time is O(1) per operation. Include code and a brief explanation of the worst-case and amortized behavior.

HardTechnical

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Implement a persistent (immutable) segment tree in C++ or Java that supports versioned point updates and range-sum queries returning historical versions. Describe node sharing to minimize memory overhead per update and estimate memory increase per version for sparse updates.

MediumTechnical

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As an SRE optimizing a critical in-memory index, compare contiguous array-based layouts versus pointer-based (linked) structures. Discuss cache locality, memory overhead, allocation patterns, fragmentation, GC impact, and how each choice affects tail latency under high load. Provide concrete examples and metrics to measure.

EasyTechnical

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Implement a simplified dynamic array (vector) in C++ from first principles. Requirements: provide constructor, destructor, push_back(T), pop_back(), operator[](size_t), size(), capacity(). Use manual memory management (new/delete or malloc/free). Ensure exception-safety and no memory leaks. Explain your resizing strategy (growth factor) and analyze amortized cost of push_back across n operations.

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