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Core
Backtracking
Backtracking is systematic search over a tree of partial solutions: at each step you choose an option, explore deeper, and undo the choice before trying the next (choose, explore, unchoose). Pruning kills branches that cannot lead to a valid solution before you waste work on them. Interviews probe it because permutations, combinations, subsets, and constraint problems (N-queens, sudoku) all share this template, and the in-place choose/unchoose pattern avoids re-allocating state at every node, which is the difference between an elegant solution and an exponential memory blowup.
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Coding & DSAExplain backtracking and use it to generate all permutations / subsets / combinations.→Coding & DSAGenerate all valid combinations of n pairs of parentheses.→ML Infrastructure & GPUsExplain knowledge distillation: how it works, when to use it, and how it compares to quantization and pruning.→Coding & DSAHow do you recognize and solve a dynamic-programming problem? Walk through one end to end.→ML Infrastructure & GPUsWhat is model pruning (and sparsity), and how does it compare to quantization and distillation?→Coding & DSASerialize and deserialize a binary tree.→
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