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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.

MediumTechnical
0 practiced
Implement Quickselect in Python to find the k-th largest element in an unsorted list in average O(n) time. Explain pivot-selection strategies (random pivot vs median-of-medians) and how they affect worst-case guarantees. Example: arr=[3,1,5,2,4], k=2 → return 4.
HardSystem Design
0 practiced
Describe algorithms to perform a join between two very large distributed tables that do not fit in memory on a single machine: hash-partitioned join, sort-merge join, and broadcast join. Explain costs in network I/O, memory, and when each approach is appropriate in Spark or MapReduce.
HardSystem Design
0 practiced
Propose algorithms for computing connected components in a massive graph that doesn't fit on a single machine. Compare distributed BFS/DFS, label propagation, and distributed union-find approaches. Discuss communication cost, convergence, and how you'd partition the graph to minimize cross-partition messages.
HardTechnical
0 practiced
You're sorting multi-terabyte datasets on a cluster with mixed SSDs and HDDs. Explain how storage characteristics (seek latency vs throughput), block sizes, and parallel I/O affect external sort strategy. Propose optimizations such as parallel run generation, selecting fan-in for k-way merge, and using SSDs for random-access tasks.
MediumTechnical
0 practiced
Implement reservoir sampling in Python to uniformly sample k items from a stream of unknown length. Explain why the algorithm produces a uniform sample and analyze its time and space complexity.

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