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Algorithms and Data Structures Questions

Comprehensive understanding of core data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs, and fundamental algorithms including sorting, searching, traversal, string manipulation, and graph algorithms. Ability to analyze and compare time and space complexity using asymptotic notation such as Big O, Big Theta, and Big Omega, and to reason about trade offs between different approaches. Skills include selecting the most appropriate data structure for a problem, designing efficient algorithms, applying algorithmic paradigms such as divide and conquer, dynamic programming, greedy methods, and graph search, and implementing correct and robust code for common interview problems. At more senior levels, this also covers optimizing for large scale through considerations of memory layout, caching, amortized analysis, parallelism and concurrency where applicable, and profiling and tuning for performance in realistic systems.

HardTechnical
149 practiced
Implement an algorithm (pseudocode) to find articulation points and bridges in a graph and then extend the approach to support online edge insertions (only additions). What data structures and amortized complexities are achievable for connectivity updates and critical edge detection?
HardTechnical
97 practiced
Explain HyperLogLog (HLL) for approximating cardinality on very large streams. Derive how relative error scales with the number of registers and discuss merging of sketches from multiple shards, memory footprint, and use cases in tracking unique users across sessions.
MediumTechnical
145 practiced
Given two large sorted arrays stored on disk, design an algorithm to compute their intersection with minimal memory usage and minimal passes over data. Describe how to handle skew and IO constraints in practice for feature joining across large tables.
EasyTechnical
133 practiced
Implement a queue using two stacks in Python supporting enqueue and dequeue in amortized O(1) time. Explain the amortized analysis and how this structure might be used to buffer batched inferences in an ML serving system.
HardTechnical
98 practiced
Given a dynamic undirected graph where edges are being added and removed, propose a data structure and algorithm to maintain connectivity queries (are u and v connected?) efficiently. Compare union-find (disjoint set) for incremental graphs vs fully dynamic approaches and discuss complexity.

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