Algorithms and Problem Solving Questions

Covers how a candidate approaches technical problems from clarifying requirements to producing correct and efficient implementations. Topics include asking clarifying questions, defining constraints, choosing and applying core data structures such as arrays, strings, trees, graphs, and hash maps, and employing algorithmic techniques including depth first search, breadth first search, dynamic programming, searching, and sorting. Candidates should be able to analyze time and space complexity, reason about edge cases and correctness, communicate trade offs clearly, and translate real world marketplace scenarios into algorithmic formulations that can be implemented and optimized.

MediumTechnical

58 practiced

Coding (Python): Implement an LRUCache class with methods get(key) and put(key, value). The cache should have a fixed capacity and both operations should be O(1) average time. Explain your design choices, memory costs, and how you would make it safe for concurrent access in a multithreaded environment.

EasyTechnical

56 practiced

Scenario: Design a 45-minute onsite coding interview for a mid-level software engineer that focuses on algorithms and problem solving. Provide problem types, time allocation, evaluation rubric, and follow-up questions to probe depth. Explain how this format balances assessment with candidate experience.

MediumTechnical

61 practiced

Problem Solving/Technical Coding: You are given k sorted singly linked lists with a total of n nodes. Describe algorithms to merge them into one sorted list, implement the most efficient approach in Python or Java, analyze time and space complexity, and explain trade-offs between using a min-heap and divide-and-conquer merging.

HardSystem Design

50 practiced

System Design/Problem Solving: For a marketplace with tens of millions of users and items, design an algorithmic recommendation pipeline that returns personalized top-N results within 100ms. Cover candidate generation (ANN, collaborative filtering, content-based), feature computation, online ranking, model serving, and the trade-offs between offline heavy computation and online personalization.

MediumTechnical

90 practiced

Technical domain-specific: Design a high-throughput in-memory counter that supports increment(), get(), and reset() under heavy concurrent access on a single machine. Describe data structures, sharding strategies to reduce contention, atomic primitives or batching techniques, and discuss eventual consistency trade-offs.

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