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.
Linked Lists and Trees
Dynamic and pointer based data structures including linked lists and tree structures commonly tested in interviews. For linked lists cover node based representation, traversal, insertion at head and tail, deletion, searching, reversing a list, detecting cycles, and tradeoffs versus array based lists. For trees cover basic concepts such as binary trees and binary search trees, tree node representation, insertion and deletion in search trees, recursion patterns, and traversal algorithms including depth first search with in order pre order and post order variants and breadth first search. Also include problem solving patterns such as recursion and iterative stack or queue based approaches, analysis of time and space complexity in plain terms, and common interview tasks such as lowest common ancestor, tree balancing awareness, and converting between representations. Practice includes implementing algorithms, writing traversal routines, and reasoning about correctness and performance.
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.
Linked Lists Stacks and Queues
Covers core singly and doubly linked list concepts and the fundamental abstract data types stack and queue. For linked lists this includes node structure, traversal, insertion at head and tail, deletion, reversal, finding middle, merging, detecting cycles, removing duplicates, intersection detection, and pointer manipulation details for languages with manual memory management. For stacks and queues this includes LIFO and FIFO semantics, push, pop, peek, enqueue, dequeue, circular buffer implementations, and implementing one with the other (for example queue with two stacks). Also includes array versus linked list implementations, complexity analysis for time and space, and common algorithmic patterns that use these structures (for example bracket matching, reverse polish notation evaluation, depth first search using a stack, breadth first search using a queue, sliding window and monotonic queue techniques). Interviewers assess correct implementation, edge case handling, performance tradeoffs, and ability to choose the appropriate structure or approach for a problem.
Handling Problem Variations and Constraints
This topic covers the ability to adapt an initial solution when interviewers introduce follow up questions, new constraints, alternative optimization goals, or larger input sizes. Candidates should quickly clarify the changed requirement, analyze how it affects correctness and complexity, and propose concrete modifications such as changing algorithms, selecting different data structures, adding caching, introducing parallelism, or using approximation and heuristics. They should articulate trade offs between time complexity, space usage, simplicity, and robustness, discuss edge case handling and testing strategies for the modified solution, and describe incremental steps and fallbacks if the primary approach becomes infeasible. Interviewers use this to assess adaptability, problem solving under evolving requirements, and clear explanation of design decisions.
Fundamental Algorithms and Techniques
Covers core algorithmic concepts and problem solving patterns commonly assessed in technical interviews. Topics include searching algorithms such as binary search; sorting algorithms such as merge sort and quick sort; graph traversal methods such as breadth first search and depth first search; recursion and divide and conquer techniques; greedy heuristics; and dynamic programming including memoization and tabulation. Also includes implementation patterns such as two pointers, sliding window, prefix sums, and divide and conquer composition, as well as practical considerations like in place versus out of place implementations, stability for sorting, recursion stack and memory usage, and amortized analysis. Candidates should be able to implement these algorithms correctly, explain correctness and trade offs, analyze time and space complexity using Big O notation for best case average case and worst case, select appropriate approaches given input constraints, combine patterns to solve composite problems, and optimize or refactor solutions while handling edge cases.
Data Structure Selection and Trade Offs
Skill in selecting appropriate data structures and algorithmic approaches for practical problems and performance constraints. Candidates should demonstrate how to choose between arrays lists maps sets trees heaps and specialized structures based on access patterns memory and CPU requirements and concurrency considerations. Coverage includes case based selection for domain specific systems such as games inventory or spatial indexing where structures like quadtrees or spatial hashing are appropriate, and language specific considerations such as value versus reference types or object pooling. Emphasis is on explaining rationale trade offs and expected performance implications in concrete scenarios.
Advanced Algorithms and Problem Solving
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.
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.