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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
19 practiced
You have per-minute counts for requests over a day. Explain how you would support fast range-sum queries and occasional updates. Compare using prefix-sum arrays versus Fenwick trees (binary indexed tree) and describe complexity tradeoffs relevant to SRE monitoring dashboards.
MediumTechnical
16 practiced
You must place N replicas of a service across data centers to minimize average client latency subject to capacity limits. Recognize this as a variant of the facility location problem which can be NP-hard. Propose greedy or local-search heuristics, analyze approximation ratios where applicable, and discuss practical considerations for SRE deployment automation.
EasyTechnical
24 practiced
Describe Dijkstra's algorithm and explain in plain terms why it requires non-negative edge weights. Relate to an SRE scenario where you choose replication routes based on latency estimates; explain what happens with negative or inconsistent measurements and how you would detect and mitigate wrong routes.
MediumTechnical
16 practiced
Implement a generator that enumerates all valid deployment sequences for services given a DAG of dependencies (i.e., enumerate all topological sorts) and stop after producing the first M sequences. Provide pseudocode or Python, ensure you handle large branching by pruning when M is reached, and discuss how this helps SREs plan rollouts.
MediumTechnical
18 practiced
Prove correctness of the greedy algorithm that schedules the maximum number of non-overlapping intervals by always selecting the interval with earliest finish time. Use an exchange argument and then explain an SRE use case such as scheduling maintenance windows to maximize throughput of maintenance actions.

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