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

56 practiced

Problem Solving: Design a data structure that supports add(x) for streaming integers and top(k) queries to return the k most frequent elements at the current time. Provide options optimized for fast top(k) queries and analyze time and space trade-offs. Sketch a Python implementation outline and discuss approximations if exact counts are too expensive.

EasyTechnical

47 practiced

Scenario: A release includes a function that used to handle inputs of size 10k but now takes quadratic time and times out on 100k. As an Engineering Manager, describe your plan to diagnose the regression, prioritize fixes, mentor the team on algorithmic improvements, and institute safeguards to prevent similar regressions.

EasyTechnical

53 practiced

Leadership: Design a concrete rubric (3-6 criteria with scoring guidance) to evaluate candidate solutions to algorithmic coding problems focusing on correctness, complexity, edge cases, code quality, and communication. Explain why you selected each criterion and how this rubric helps calibrate interviewers across a hiring panel.

MediumTechnical

51 practiced

Coding: Given the root of a binary tree, implement is_balanced(root) that returns True if the tree is height-balanced (difference between heights of left and right subtrees is at most 1 for every node). Aim for O(n) time and explain your testing and recursion-depth considerations for production code.

HardSystem Design

67 practiced

System Design/Problem Solving: You manage a compute cluster that must schedule incoming jobs with DAG dependencies, heterogeneous resource requirements (CPU, memory, GPU), and soft/hard deadlines. Jobs arrive continuously. Propose a scheduling algorithm or strategy to maximize throughput and meet as many deadlines as possible. Include heuristics for admission control, preemption, and how to evaluate scheduler performance in production.

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