Recursion and Dynamic Programming Questions

Covers recursive problem solving and dynamic programming as core algorithmic techniques. For recursion, understand how functions call themselves, base and recursive cases, the call stack, common patterns such as tree and graph traversals, backtracking, permutations, and detecting and avoiding infinite recursion. For dynamic programming, understand when to apply optimization via memoization and bottom up approaches, recognize optimal substructure and overlapping subproblems, convert naive recursive solutions into memoized or tabulated solutions, and analyze time and space complexity tradeoffs. Familiarity with classic examples such as Fibonacci, longest common subsequence, knapsack, coin change, and path counting is expected. At more senior levels, be able to discuss performance considerations, space optimization, and how DP principles can map onto real systems such as caching strategies, state management, and optimization of workflows or database query plans.