Algorithm Design and Analysis Questions

Covers algorithmic problem solving and analysis fundamentals required in technical interviews. Topics include common data structures, sorting and searching, recursion and divide and conquer, dynamic programming, greedy strategies, backtracking, graph algorithms such as breadth first search and depth first search, shortest path and topological sort, string algorithms, and techniques for deriving correct and efficient solutions. Candidates should demonstrate ability to reason about correctness, derive time and space complexity bounds using Big O notation, and discuss scalability and optimization trade offs for large inputs.

MediumTechnical

0 practiced

Implement an N-Queens solver in Python that returns all distinct board configurations for placing N queens so that no two attack each other. Use backtracking with pruning using sets for occupied columns and diagonals. Explain pruning optimizations, symmetry-breaking to reduce work, and analyze worst-case search complexity.

HardTechnical

0 practiced

Design an algorithmic approach to influence maximization in a social network under the Independent Cascade model: choose k seed nodes to maximize expected spread. Explain why the problem is NP-hard, outline the greedy approximation with submodularity guarantees, and discuss scalable approximations such as CELF and IMM/TIM+ for large graphs.

MediumTechnical

0 practiced

Implement Dijkstra's algorithm in Python to compute shortest path distances from a source node to all nodes in a directed graph with non-negative weights given as adjacency lists graph = {u: [(v,w), ...]}. Return a dict of distances and explain time complexity using a binary heap vs Fibonacci heap and the practical trade-offs for sparse graphs.

MediumTechnical

0 practiced

Given a directed graph representing task dependencies, implement an algorithm in Python to either produce a valid topological ordering or detect and return a cycle when none exists. Compare Kahn's algorithm using indegrees to a DFS-based approach and discuss how this applies to scheduling ML pipeline steps with dependencies.

EasyTechnical

0 practiced

Explain what makes a sorting algorithm 'stable' and why stability matters when sorting multi-key datasets in data pipelines (for example, sort by country then income preserving earlier order). Provide examples of stable and unstable algorithms and show how to do multi-key stable sorts in Python efficiently.

Unlock Full Question Bank

Get access to hundreds of Algorithm Design and Analysis interview questions and detailed answers.

Join thousands of developers preparing for their dream job.