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

144 practiced

Implement the Knuth-Morris-Pratt (KMP) algorithm in Python to find all occurrences of a pattern in a text and return starting indices. Explain how you compute the prefix-function (lps array) and why overall runtime is O(n + m).

HardSystem Design

138 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.

MediumTechnical

74 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.

MediumSystem Design

94 practiced

Design an external merge sort to sort a 1 TB file on a machine with 16 GB RAM and disk storage. Explain run generation (how large each run should be), k-way merge strategy, buffer sizing, and how to choose k given memory constraints and I/O characteristics.

EasyTechnical

100 practiced

Given an unsorted list of integers, implement a Python function that detects whether any value appears more than once using a hash table (set or dict). The solution should be expected O(n) time and O(n) space. Discuss worst-case hash behavior and mitigation strategies for adversarial inputs.

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