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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
Model scheduling N maintenance tasks on M exclusive resources as a maximum bipartite matching problem. Describe the graph reduction to max flow, sketch Dinic's algorithm or Hopcroft-Karp for bipartite matching, provide complexity, and discuss how you'd implement and scale this for hundreds of thousands of tasks in SRE automation.
HardTechnical
0 practiced
Design a parallel algorithm to compute prefix sums (scan) on streaming time-series data on a multicore aggregator. Explain chunking, local scans, combining sums, synchronization mechanisms, and how to make the design fault-tolerant against worker failures while keeping high throughput for SRE metric pipelines.
MediumTechnical
0 practiced
Given two sorted arrays of sizes m and n, find the median of the combined set in O(log(min(m,n))) time. Provide the algorithm, explain partitioning logic, handle even/odd total lengths, and discuss edge cases relevant to merging sorted logs from distributed sources in SRE work.
MediumTechnical
0 practiced
Implement Rabin-Karp rolling hash substring search to detect an error signature in a stream of logs. Describe choosing modulus and base to minimize collisions, present rolling update formula, and discuss how to extend to multiple patterns or streaming inputs and implications for SRE alerting pipelines.
EasyTechnical
0 practiced
Explain how hash collisions are handled in hash table implementations. Compare chaining and open addressing in terms of cache behavior, average lookup time, deletion complexity, and load factor, particularly for SRE use cases like high-speed in-memory caches handling bursts.

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