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.
Advanced Graph Algorithms
Higher level and combined graph algorithm topics frequently expected at senior or competitive programming levels. Topics include strongly connected components and algorithms such as Kosaraju and Tarjan, minimum spanning trees using Kruskal and Prim with Union Find optimizations, network flow fundamentals including Ford Fulkerson and Edmonds Karp and applications to bipartite matching, graph reductions and transformations, graph coloring and bipartite checks, advanced traversal techniques such as bidirectional search and multi source traversals, and strategies to combine algorithms for complex problems. Emphasize time and space complexity, algorithm correctness proofs, implementation pitfalls, and when to prefer one algorithm or data structure over another.
Game Algorithms and Problem Solving
Covers algorithmic and systems problems encountered in game development and real time simulation. Candidates should demonstrate how core computer science fundamentals map to practical gameplay and engine concerns, including pathfinding with A Star and related heuristics and optimizations, spatial partitioning and culling using structures such as quadtrees, octrees, and k dimensional trees, frustum culling, collision detection and response techniques including bounding volume hierarchies and continuous collision approaches, physics simulation approximations and numerical stability trade offs, particle systems, inventory and resource management, dialogue tree parsing, game state machines and transitions, efficient state serialization, snapshotting and delta compression, and networked game concerns such as latency compensation, synchronization, client authority versus server authority, interpolation and extrapolation. Assessment emphasizes algorithmic complexity and space and time trade offs, performance profiling and optimization for central processing unit and memory constrained environments, memory layout and cache friendliness, concurrency and multithreading strategies, deterministic simulation and lockstep architectures, debugging and testing deterministic systems, event driven designs, and designing maintainable engine or gameplay systems that balance correctness, responsiveness, scalability, and developer ergonomics. Candidates should be prepared to explain algorithms, analyze complexity, justify engineering trade offs, describe profiling methodology, and propose targeted optimizations to meet gameplay performance and scalability requirements.
Basic Game Development Patterns
Understand and explain common game development patterns and when to use them. Core topics include object pooling for memory and allocation control, event systems such as publisher subscriber or observer patterns for decoupled communication, input handling strategies, game state management, coroutine and asynchronous patterns in modern engines, and spatial data structures such as quadtrees or spatial hashing for performance. Candidates should be able to implement, reason about complexity and memory trade offs, and describe optimizations or debugging approaches under time pressure.
Rendering and GPU Programming
Authoring and optimizing rendering code and shaders and understanding graphics processing unit constraints and trade offs. Candidates should be able to write and reason about vertex fragment and compute shaders in languages such as High Level Shading Language OpenGL Shading Language or platform specific shading languages discuss shader instruction costs texture lookups branching overhead memory bandwidth and texture compression strategies reduce draw calls through batching and texture atlasing implement level of detail systems and streaming and profile rendering workloads across platforms including tile based renderers on mobile.
Graphics and Animation Systems
Graphics pipeline architecture (vertices, shaders, rasterization), rendering optimization techniques (batching, sorting, view frustum culling), animation systems including skeletal animation and blend trees, particle systems and visual effects, and integration strategies with modern game engines.
Debugging Testing and Optimization
Core engineering skills for identifying, diagnosing, testing, and improving code correctness and performance. Covers approaches to finding and fixing bugs including reproducible test case construction, logging, interactive debugging, step through debugging, and root cause analysis. Includes testing strategies such as unit testing, integration testing, regression testing, test driven development, and designing tests for edge cases, boundary conditions, and negative scenarios. Describes performance optimization techniques including algorithmic improvements, data structure selection, reducing time and space complexity, memoization, avoiding unnecessary work, and parallelism considerations. Also covers measurement and verification methods such as benchmarking, profiling, complexity analysis, and trade off evaluation to ensure optimizations preserve correctness and maintainability.
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.
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.
Gameplay Mechanics Implementation
Implementing core gameplay mechanics including player input and movement systems, collision detection and response, physics integration, damage and health systems, scoring and win lose conditions, game state management, and event driven logic. Candidates should explain data structures and algorithms used for entity management, spatial partitioning, collision queries, finite state machines for game objects, and timing models such as fixed timestep and interpolation. For multiplayer contexts discuss authoritative server design, client prediction, latency compensation, rollback or deterministic simulation approaches, and synchronization strategies. The topic also covers code architecture choices such as entity component systems versus object oriented designs, handling edge cases like simultaneous collisions or rapid input, performance optimization, instrumentation and debugging techniques, and approaches to testing gameplay logic and ensuring deterministic behavior where required.