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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
0 practiced
Propose and implement (or describe algorithmically) transformations to improve cache locality for pointer-based binary tree traversals, such as the van Emde Boas (vEB) layout. Explain how you map nodes to an array, why locality improves, and trade-offs when the tree is updated frequently.
EasyTechnical
0 practiced
In Python, implement a stack that supports push(x), pop(), top(), and getMin() — where getMin() returns the minimum element in the stack — all in O(1) time. Your API should handle duplicate values and empty-stack errors gracefully. Provide a short example of usage.
MediumSystem Design
0 practiced
Design an eviction policy combining LRU and LFU characteristics (e.g., W-TinyLFU) for a model feature cache used in ML serving. Explain how you would track recency and frequency, where to keep admission filters, admission windows, and how to tune parameters subject to memory constraints and skewed access distributions.
HardTechnical
0 practiced
Implement suffix array construction using a linear-time algorithm (SA-IS or skew/DC3) in C++ or explain in detail how it works and why it runs in O(n). Provide complexity analysis and discuss memory requirements and practical considerations for very long strings.
EasyTechnical
0 practiced
Implement a basic hash table using separate chaining in either Java or C++. The table should support insert(key,value), erase(key), find(key) and should resize (rehash) when load factor exceeds a threshold. Describe your collision resolution choice, resizing strategy, and memory/performance trade-offs for chaining vs open addressing.

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