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Advanced Algorithms and Problem Solving Questions

Comprehensive assessment of advanced algorithmic reasoning, design, and optimization for hard and composite problems. Covers advanced dynamic programming techniques including state compression and bitmask dynamic programming, combinatorial generation and backtracking, recursion and divide and conquer strategies, greedy algorithms with correctness proofs, and advanced graph algorithms such as breadth first search, depth first search, shortest path algorithms including Dijkstra and Bellman Ford, minimum spanning tree, network flow, strongly connected components, and topological sort. Also includes advanced tree and string algorithms such as suffix arrays and advanced hashing, bit manipulation and low level optimizations, algorithmic reductions and heuristics, and complexity analysis including amortized reasoning. Candidates should recognize applicable patterns, combine multiple data structures in a single solution, transform brute force approaches into optimized solutions, prove correctness and derive time and space complexity bounds, handle edge cases and invariants, and articulate trade offs and incremental optimization strategies. At senior levels expect mentoring on algorithmic choices, designing for tight constraints, and explaining engineering implications of algorithm selection.

EasyTechnical
16 practiced
Implement an algorithm in Python to check if two binary trees are mirror images of each other. Requirements: O(n) time, O(h) auxiliary space (h = height). Discuss recursion depth issues for skewed trees and show an iterative stack-based alternative.
HardSystem Design
19 practiced
Design a parallel external-memory algorithm to construct a suffix array for terabyte-scale corpora used to build language model token indices. Outline I/O patterns, memory usage per node, parallelization strategy (e.g., map-reduce / MPI), and verification steps to ensure correctness. Discuss challenges like load balancing and suffix comparisons across partition boundaries.
EasyTechnical
22 practiced
Define what a greedy algorithm is and give a correctness proof sketch for the greedy algorithm used to build an optimal prefix-free code (Huffman coding). As an AI Engineer, explain when greedy algorithms fail and how you would detect that a greedy choice is not optimal for a given optimization in ML (e.g., feature pruning).
EasyTechnical
18 practiced
Describe what a suffix array is and at a high level how it's constructed. Give one practical NLP application where suffix arrays are preferable to suffix trees because of memory constraints, and explain the role of the LCP array in queries like 'longest repeated substring'.
MediumTechnical
20 practiced
Design a meet-in-the-middle algorithm to solve subset-sum when n <= 40. Describe the steps, time/space complexity, and how you'd use the approach in feature subset selection or constrained combinatorial search for an AI application. Include ideas for pruning and parallelization.

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