Coding Fundamentals and Problem Solving Questions

Focuses on algorithmic thinking, data structures, and the process of solving coding problems under time constraints. Topics include core data structures such as arrays, linked lists, hash tables, trees, and graphs, common algorithms for searching and sorting, basics of dynamic programming and graph traversal, complexity analysis for time and space, and standard coding patterns. Emphasis on a disciplined problem solving approach: understanding the problem, identifying edge cases, proposing solutions with trade offs, implementing clean and readable code, and testing or reasoning about correctness and performance. Includes debugging strategies, writing maintainable code, and practicing medium difficulty interview style problems.

HardTechnical

0 practiced

Design and implement Tarjan's algorithm in Python to compute strongly connected components (SCCs) of a directed graph represented as an adjacency list. Return SCCs as lists of nodes, explain lowlink values, and discuss recursion limits and iterative alternatives for very large graphs.

EasyTechnical

0 practiced

Describe how you'd write unit tests for a data preprocessing function normalize_and_fill(dataframe) that normalizes numerical columns and fills missing values. List edge cases and provide 3 pytest-style test examples (constant column, all-NaN column, extremely large values).

MediumTechnical

0 practiced

Implement Longest Increasing Subsequence (LIS) length in Python with O(n log n) time using the tails / patience sorting technique. Explain the algorithm and how to reconstruct the actual subsequence. Discuss trade-offs versus the O(n^2) DP approach.

HardTechnical

0 practiced

Implement edit_distance(s, t) in Python computing Levenshtein distance (insert/delete/replace). Optimize space to O(min(n,m)) using rolling arrays, explain how to reconstruct an alignment path, and discuss optimizations for long sequences (e.g., Ukkonen's algorithm).

EasyTechnical

0 practiced

Write a maintainable Python decorator @timeit that measures wall-clock time of a function and logs the execution duration. Demonstrate usage on a sample function and preserve metadata (name, docstring). Also mention how to support async functions and optional logging levels.

Unlock Full Question Bank

Get access to hundreds of Coding Fundamentals and Problem Solving interview questions and detailed answers.

Join thousands of developers preparing for their dream job.