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.
Algorithmic Patterns
Familiarity with common algorithmic and problem solving patterns such as two pointer techniques, sliding window, binary search, breadth first search and depth first search, common greedy heuristics, and introductory dynamic programming. Candidates should be able to identify which pattern applies to a given problem, explain how to adapt the pattern for constraints, and describe trade offs between naive and optimized approaches in time and memory usage.
Tree Traversal and Tree Problems
Problems and patterns involving tree structures, including traversal orders such as in order, pre order, and post order, recursive and iterative traversal techniques, computing tree height and depth, lowest common ancestor solutions, path sum and root to leaf problems, and balancing considerations. Candidates should be able to reason about recursion depth, stack based iterative solutions, and analyze time and memory complexity for tree algorithms.
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.
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.
Array and String Manipulation
Comprehensive coverage of language level operations and algorithmic techniques for arrays and strings that are commonly evaluated in coding interviews. Candidates should understand common language methods for arrays and strings, including their parameters and return values, chaining of operations, and the implications of mutable versus immutable types for in place versus extra space solutions. Core algorithmic patterns include iteration and traversal, index based and pointer based approaches, two pointer strategies, sliding window, prefix and suffix sums, sorting and partitioning, and cumulative or running sums. Problem classes include traversal, insertion and deletion, reversing and rotating, merging and deduplicating, subarray and substring search, anagram detection, palindrome detection, longest substring and maximum subarray problems, and pointer based reordering and partitioning tasks. Pattern matching techniques include naive matching, Knuth Morris Pratt and rolling hash approaches, and hashing for frequency and membership checks. String transformation and comparison topics include edit distance, sequence transformation problems such as word ladder, and parsing and validation tasks. Candidates should be prepared to implement correct and efficient solutions in common programming languages, reason about time and space complexity, optimize for input size and memory constraints, handle edge cases such as empty inputs and boundary conditions, and address character level concerns such as encoding differences, multibyte characters, surrogate pairs and unicode normalization. Interviewers may probe language specific implementation details, in place mutation versus copying, fixed buffer strategies, streaming or incremental algorithms for large inputs, and trade offs between clarity and performance. Expect questions that require selecting the right algorithmic pattern, implementing a robust solution, and justifying complexity and memory decisions.
Linked Lists and Pointer Manipulation
Comprehensive knowledge of linked list data structures and pointer based implementations. Covers singly linked lists, doubly linked lists, circular lists, and node based sequential structures. Candidates should be able to implement and reason about core operations including traversal, insertion, deletion, reversing a list, finding the middle element, removing the nth node from the end, detecting and removing cycles, merging sorted lists, partitioning lists, computing intersection nodes, and other in place transformations. Emphasize pointer and reference manipulation techniques, manual memory allocation and deallocation, ownership and lifetime considerations, and debugging strategies for pointer errors and memory leaks, particularly in manual memory management languages such as C and C plus plus. Also cover implementation techniques such as iterative and recursive approaches, use of dummy head or sentinel nodes to simplify edge cases, and in place algorithms to minimize extra memory. Discuss algorithmic complexity and trade offs relative to contiguous arrays, including dynamic resizing, memory locality, and cache behavior, and when linked lists are the appropriate abstraction such as in embedded systems or when implementing free lists and adjacency lists. Interviewers may probe both low level pointer manipulation and higher level reasoning about when to use list based structures and how list concepts extend into more complex data structures.
Algorithmic Problem Solving
Evaluates ability to decompose computational problems, design correct and efficient algorithms, reason about complexity, and consider edge cases and correctness. Expectation includes translating problem statements into data structures and algorithmic steps, justifying choices of approach, analyzing time and space complexity, optimizing for constraints, and producing test cases and proofs of correctness or invariants. This topic covers common algorithmic techniques such as sorting, searching, recursion, dynamic programming, greedy algorithms, graph traversal, and trade offs between readability, performance, and maintainability.