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.
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.
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.
Operating System Fundamentals
Comprehensive knowledge of operating system concepts and practical administration across Linux, Unix, and Windows platforms. Core theoretical topics include processes and threads, process creation and termination, scheduling and context switching, synchronization and deadlock conditions, system calls, kernel versus user space, interrupt handling, memory management including virtual memory, paging and swapping, and input and output semantics including file descriptors. Practical administration and tooling expectations include file systems and permission models, user and group account management, common system utilities and commands such as grep, find, ps, and top, package management, service and process management, startup and boot processes, environment variables, shell and scripting basics, system monitoring, and performance tuning. Platform specific knowledge should cover Unix and Linux topics such as signals and signal handling, kernel modules, initialization and service management systems, and command line administration, as well as Windows topics such as the registry, service management, event logs, user account control, and graphical and command line administration tools. Security and infrastructure topics include basic system hardening, common misconfigurations, and an understanding of containerization and virtualization at the operating system level. Interview questions may probe conceptual explanations, platform comparisons, troubleshooting scenarios, or hands on problem solving.
Sorting and Searching Algorithms
Core computer science algorithms for ordering and locating data, including understanding, implementing, and applying common sorting algorithms and search techniques and analyzing their performance. Candidates should know comparison sorts such as merge sort, quick sort, heap sort, insertion sort, selection sort, and bubble sort and understand stability, in place versus out of place behavior, and best average and worst case time and space complexities. They should master binary search and linear search and variations and know when searching requires a different approach. Knowledge should include algorithmic patterns such as divide and conquer and two pointers, selection algorithms such as quickselect and nth element, and non comparison sorts such as counting sort, radix sort and bucket sort when appropriate. Candidates must be able to implement clean iterative or recursive versions, reason about recursion depth and stack usage, explain trade offs between using built in language sort utilities and custom implementations, and choose the right algorithm for a problem based on input size, memory constraints, and stability requirements. Interviewers often assess coding correctness, complexity analysis using big O notation, edge cases, comparator usage for custom ordering, and ability to justify algorithm choices.
Complexity Analysis and Tradeoffs
Evaluating algorithmic complexity and engineering trade offs between time, space, maintainability, and operational cost. Candidates should be able to express complexity using big O notation, reason about amortized cost, identify bottlenecks, compare alternative solutions and explain when to optimize or accept higher complexity. Include discussion of measurement, profiling, and pragmatic trade offs such as caching versus recomputation or memory versus latency.
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.
Operating System Concepts
Covers general operating system and process management fundamentals applicable to desktop, server, and embedded contexts. Topics include processes versus threads, process lifecycle operations (fork, exec, exit, wait), process and thread states, context switching, scheduling policies and priorities, CPU and memory management, virtual memory and paging, resource utilization and load metrics, common operating system diagnostic and management tools and commands, and trade offs between process isolation and shared memory threading.
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.