Technical Fundamentals & Core Skills Topics
Core technical concepts including algorithms, data structures, statistics, cryptography, and hardware-software integration. Covers foundational knowledge required for technical roles and advanced technical depth.
Data Structures and Complexity
Comprehensive coverage of fundamental data structures, their operations, implementation trade offs, and algorithmic uses. Candidates should know arrays and strings including dynamic array amortized behavior and memory layout differences, linked lists, stacks, queues, hash tables and collision handling, sets, trees including binary search trees and balanced trees, tries, heaps as priority queues, and graph representations such as adjacency lists and adjacency matrices. Understand typical operations and costs for access, insertion, deletion, lookup, and traversal and be able to analyze asymptotic time and auxiliary space complexity using Big O notation including constant, logarithmic, linear, linearithmic, quadratic, and exponential classes as well as average case, worst case, and amortized behaviors. Be able to read code or pseudocode and derive time and space complexity, identify performance bottlenecks, and propose alternative data structures or algorithmic approaches to improve performance. Know common algorithmic patterns that interact with these structures such as traversal strategies, searching and sorting, two pointer and sliding window techniques, divide and conquer, recursion, dynamic programming, greedy methods, and priority processing, and when to combine structures for efficiency for example using a heap with a hash map for index tracking. Implementation focused skills include writing or partially implementing core operations, discussing language specific considerations such as contiguous versus non contiguous memory and pointer or manual memory management when applicable, and explaining space time trade offs and cache or memory behavior. Interview expectations vary by level from selecting and implementing appropriate structures for routine problems at junior levels to optimizing naive solutions, designing custom structures for constraints, and reasoning about amortized, average case, and concurrency implications at senior levels.
Binary Trees and Binary Search Trees
Focuses on tree data structures, specifically binary trees and binary search trees. Candidates should understand node relationships, common traversals including in order, pre order, post order, and level order, and be able to implement traversals both recursively and iteratively. Cover binary search tree properties and operations including search, insertion, deletion, validation of binary search tree property, and finding the lowest common ancestor. Include problems on tree paths, height and balance calculations, serialization and deserialization, checking and restoring balance at a high level, and use cases in system design. Emphasize complexity analysis, recursion versus iterative solutions using stacks or queues, and handling edge cases such as duplicate keys and degenerate trees.
Arrays and Hash Map Operations
Covers algorithmic patterns that use arrays together with hash based maps or dictionaries to achieve efficient lookup and counting. Topics include frequency counting, duplicate detection, two sum and k sum variants, sliding window with counts, index mapping, grouping by keys, and using hash maps to reduce time complexity from quadratic to linear. Emphasize insertion deletion and lookup costs, collision and memory considerations, trade offs between using hash maps versus sorting or two pointer techniques, and typical interview problem families that rely on combining arrays with associative containers.
Practical Coding Under Pressure
Test the candidate on solving coding problems under time and information constraints while communicating clearly. Topics include asking clarifying questions making and stating reasonable assumptions decomposing the problem outlining a plan writing minimally correct code adding tests handling edge cases and then iterating to improve performance. Interviewers evaluate time management debugging approach graceful recovery from mistakes and clarity of explanation.
Graph Algorithms and Traversal
Covers fundamental representations, traversal techniques, and classical algorithms for graph structured data. Candidates should understand graph representations such as adjacency list and adjacency matrix and the tradeoffs in time and space for each. Core traversal skills include implementing and reasoning about breadth first search and depth first search for reachability, traversal order, and unweighted shortest path discovery, as well as tree traversal variants and their relationship to graph traversals. Algorithmic topics include cycle detection, topological sorting for directed acyclic graphs, connected components and strongly connected components, and shortest path and pathfinding algorithms for weighted graphs including Dijkstra algorithm and Bellman Ford algorithm with discussion of negative weights and appropriate use cases. Candidates should be able to analyze time and space complexity, choose appropriate auxiliary data structures such as queues, stacks, priority queues, and union find, handle directed versus undirected and weighted versus unweighted graphs, discuss implementation details and trade offs, and explain practical applications such as dependency resolution, scheduling, pathfinding, connectivity queries, and roles of graph algorithms in system design and data processing.
Trees and Graphs
Comprehensive knowledge of tree and graph data structures and algorithms commonly tested in coding interviews. Candidates should understand representations such as adjacency list and adjacency matrix and when to use each, and tree representations including n ary trees and binary search trees. Expect to implement and reason about traversals including depth first search and breadth first search, tree traversals such as pre order in order and post order, and level order traversal. Cover algorithms including topological sorting for directed acyclic graphs, cycle detection, connected components, shortest path algorithms such as breadth first search for unweighted graphs, Dijkstra for nonnegative weights, and Bellman Ford for graphs with negative edges, and minimum spanning tree algorithms such as Kruskal and Prim. Include disjoint set union find for connectivity and for use with Kruskal, lowest common ancestor techniques and implementations, tree dynamic programming problems, serialization and deserialization, reconstruction from traversals, balancing and validation checks for binary search trees and balanced tree concepts, diameter and path sum problems, and common interview patterns such as path finding dependency resolution and structural transformation. Emphasize implementation details and common pitfalls including correct use of visited tracking recursion depth edge cases and disconnected components, and practice articulating time and space complexity tradeoffs and algorithm selection under different constraints.
Problem Decomposition
Break complex problems into smaller, manageable subproblems and solution components. Demonstrate how to identify the root problem, extract core patterns, choose appropriate approaches for each subproblem, sequence work, and integrate partial solutions into a coherent whole. For technical roles this includes recognizing algorithmic patterns, scaling considerations, edge cases, and trade offs. For non technical transformation work it includes logical framing, hypothesis driven decomposition, and measurable success criteria for each subcomponent.
Stacks and Queues
Implementation and usage of stack and queue data structures. Problems involving balanced parentheses, expression evaluation, queue operations, or sliding window maximums. Understanding when to use each structure and their time/space trade-offs.
Problem Solving and Analytical Thinking
Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.