Data-Centric Algorithmic Problem Solving Questions
Foundational algorithm design and data-structure concepts with an emphasis on data-centric problem solving. Covers algorithmic paradigms (e.g., greedy, dynamic programming, divide-and-conquer, graph algorithms), data structures, complexity analysis, and practical approaches to solving computational problems using data.
HardTechnical
0 practiced
Implement a simple PageRank power-iteration in Python for a directed graph represented as adjacency lists. Handle dangling nodes and accept parameters: damping factor and convergence tolerance. Discuss convergence guarantees and runtime per iteration.
HardSystem Design
0 practiced
Design a distributed algorithm to compute connected components on a graph with billions of edges. Discuss frameworks (Pregel/Giraph/Spark GraphX), partitioning strategies, communication patterns, and how to minimize cross-partition messaging and memory footprint.
HardSystem Design
0 practiced
You are joining an events table with a user_profile table and observe extreme skew on a few hot user_ids causing slow stragglers. Propose algorithmic and engineering remedies (salting, replicated small-side, skew-aware partitioning) and explain trade-offs in network I/O and correctness.
HardTechnical
0 practiced
Given a feature engineering pipeline that performs expensive joins and aggregations on a large dataset daily, propose algorithmic optimizations to speed it up (e.g., pre-aggregation, incremental computation, materialized views). Include trade-offs (staleness vs latency) and implementation considerations.
MediumTechnical
0 practiced
Explain Dijkstra's algorithm for shortest paths on non-negative weighted graphs. Provide the runtime using an efficient priority queue and explain when Dijkstra is inappropriate (and what to use instead). Also discuss memory considerations for large sparse graphs.
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