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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.

MediumTechnical
0 practiced
You must compute DP[r] = min_{l<r} DP[l] + cost(l+1, r) for r=1..N where cost satisfies quadrangle inequality (convexity-like property). Describe how divide-and-conquer DP optimization reduces complexity, provide pseudocode for the optimized O(N log N) solution assuming monotonicity of optimal decisions, and explain practical tests to verify monotonicity on real cost functions.
HardTechnical
0 practiced
Design a distributed quantile estimation system for high-throughput telemetry using t-digest. Explain how to construct per-node t-digests, merge them with bounded error, choose compression parameters to achieve ≤0.5% error across 1000 nodes, and analyze memory, CPU, and merge-time trade-offs. Compare t-digest to GK and reservoir-sampling-based quantile estimators for this use case.
MediumTechnical
0 practiced
Implement a k-way merge function in Python that merges k sorted input streams (file-like iterators) into a single sorted output using at most B lines memory for buffering. Provide code or clear pseudocode, analyze time complexity (in terms of total elements and k), discuss disk IO characteristics, and explain how replacement selection can improve initial run length when generating sorted runs for external sort.
MediumTechnical
0 practiced
Model data transformation quality as edge weights that may be negative to represent data degradation. Describe how Bellman-Ford computes shortest paths and detects negative cycles in this directed graph. Analyze Bellman-Ford's time complexity, practical optimizations for sparse graphs (early stop, queue-based SPFA), and how you'd interpret detected negative cycles in a pipeline context.
EasyTechnical
0 practiced
Implement Union-Find (disjoint-set) in Python with union-by-rank and path compression. Use it to compute connected components for a dataset lineage graph. Analyze the amortized time per operation, explain memory requirements, and discuss strategies to handle very large, sparse node IDs (for example where IDs are 64-bit and not contiguous).

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