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.
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.
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.
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.
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.
Problem Solving and Structured Thinking
Focuses on general problem solving strategies and structured thinking applicable to engineering, coding, and complex decision making. Core skills include clarifying the problem, breaking problems into subproblems, identifying patterns, selecting appropriate approaches and data structures, developing and testing incremental solutions, analyzing trade offs, reasoning about time and space complexity, handling edge cases, and communicating thought process clearly. Includes algorithmic patterns and design of systematic approaches to unfamiliar problems as well as frameworks for organizing thought under ambiguity.
Algorithm Design and Dynamic Programming
Comprehensive topic covering algorithm design with a strong emphasis on dynamic programming across beginner to advanced levels. Candidates should be able to recognize overlapping subproblems and optimal substructure, define states and derive recurrence relations, and implement correct top down memoization or bottom up tabulation. Core problem types include Fibonacci and climbing stairs for basics, coin change and basic knapsack, intermediate patterns such as longest increasing subsequence, longest common subsequence, edit distance, and matrix chain multiplication, and advanced domains including bitmask dynamic programming, dynamic programming on trees, digit dynamic programming, game theoretic dynamic programming, and multi dimensional state spaces. Evaluation includes space and time optimization techniques such as rolling arrays, state compression, reducing dimensionality, and other algorithmic optimizations including divide and conquer optimization, monotone queue optimization, and convex hull trick when applicable. Candidates are expected to refactor brute force solutions into efficient dynamic programming implementations, reason about correctness and complexity, discuss trade offs between clarity and performance, and leverage related algorithmic building blocks such as binary search, common sorting algorithms, greedy strategies, and appropriate data structures to improve solutions.