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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
Explain the A* algorithm and design an admissible heuristic suitable for routing in an overlay network where edges have estimated latencies. Explain when A* will reduce to Dijkstra and how heuristic tightness affects performance and correctness for SRE path selection tooling.
HardTechnical
0 practiced
Design a streaming quantile algorithm suitable for high-throughput latency metrics with strict memory limits (e.g., 100MB). Describe and compare Greenwald-Khanna and t-digest, implement the core idea in pseudocode, explain error bounds, and explain how to merge summaries across shards in SRE architectures.
HardSystem Design
0 practiced
You must route traffic demands across a capacitated network with per-unit costs to minimize total cost while satisfying demands. Formulate the problem as min-cost max-flow, explain successive shortest path and cycle-canceling algorithms, discuss potentials (Johnson) for speed, and explain how to scale or approximate this in large SRE network optimization problems.
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.
HardTechnical
0 practiced
Design an algorithm to find the minimal set of nodes (services) whose removal would break the dependency graph into at least k disconnected components. Frame this as a vertex-cut problem, discuss reductions to edge cuts via node-splitting and max-flow, analyze complexity, and propose heuristics for large-scale SRE graphs.

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