Programming Languages & Core Development Topics
Programming languages, development fundamentals, coding concepts, and core data structures. Includes syntax, algorithms, memory management at a programming level, asynchronous patterns, and concurrency primitives. Also covers core data manipulation concepts like hashing, collections, error handling, and DOM manipulation for web development. Excludes tool-specific proficiency (see 'Tools, Frameworks & Implementation Proficiency').
Concurrency and Synchronization
Covers the principles and practical techniques for safely coordinating concurrent execution and access to shared resources. Topics include models of concurrency such as threads, processes, interrupt handlers, and tasks in a real time operating system; differences between preemptive and cooperative scheduling; shared data hazards including race conditions and read modify write hazards; critical sections and approaches to protect them including disabling interrupts in embedded contexts and scoped locks. Synchronization primitives and patterns are included: mutexes, binary semaphores, counting semaphores, condition variables, message queues, atomic operations and lock free primitives such as compare and swap. Memory ordering concerns and memory barrier usage on multi core systems are covered, along with priority inversion and priority inheritance. Also addressed are deadlock, livelock, and starvation concepts and avoidance strategies, granularity and performance trade offs of locking, and practical synchronization patterns. Preparation should include identifying and fixing races in code, designing correct concurrent interfaces, and debugging and testing techniques such as stress testing, instrumentation, deterministic replay, race detectors, static analysis, and code review strategies.
Python Programming & ML Libraries
Python programming language fundamentals (syntax, data structures, control flow, error handling) with practical usage of machine learning libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch for data manipulation, model development, training, evaluation, and lightweight ML tasks.
Error Handling and Code Quality
Focuses on writing production quality code and scripts that are defensive, maintainable, and fail gracefully. Covers anticipating and handling failures such as exceptions, missing files, network errors, and process exit codes; using language specific constructs for error control for example try except blocks in Python or set minus e patterns in shell scripts; validating inputs; producing clear error messages and logs; and avoiding common pitfalls that lead to silent failures. Also includes code quality best practices such as readable naming and code structure, using standard libraries instead of reinventing functionality, writing testable code and unit tests, and designing for maintainability and observability.
Python Fundamentals and Problem Solving
Comprehensive knowledge of the Python programming language, idiomatic usage, and the ability to implement correct, readable, and testable solutions to coding problems. Core language elements include syntax and semantics, primitive and composite data types such as integers, floats, strings, lists, dictionaries, sets, and tuples, sequence and mapping operations, control flow constructs, functions and closures, and object oriented programming basics including classes, instances, inheritance, and special methods. Additional practical topics include error and exception handling, file input and output operations, comprehensions and generator expressions, generator functions and iteration protocols, context managers, lambda functions, unpacking, and common standard library utilities. Candidates should understand algorithmic time and space complexity for common operations, typical performance characteristics of lists and dictionaries, and common pitfalls such as mutable default arguments and shared mutable state. Interview focused expectations include writing clean correct code without editor assistance, sensible variable naming, implementing basic algorithms and data structure manipulations under time constraints, reasoning about tradeoffs and complexity, and demonstrating testability and code quality.
Core Data Structures
Fundamental built in data structures used in everyday programming and coding interviews, focusing on lists, strings, and dictionaries. For lists cover indexing, slicing, iteration, common mutation operations such as append and extend, common algorithms such as sorting and reversing, and memory and performance implications. For strings cover indexing, slicing, common methods such as split, join, strip, replace, and approaches to string manipulation and pattern processing. For dictionaries cover key value semantics, insertion and lookup, iteration patterns, methods for safe access, and using dictionaries as hash tables for counting and grouping. Candidates should also know the time complexity of common operations expressed in plain terms such as constant time, linear time, and quadratic time, and be able to choose the appropriate structure for a problem and reason about space and performance tradeoffs. Practice often includes implementation level manipulations, common interview problems such as two sum and frequency counting, and writing clear code using these structures.
Algorithm Implementation and Data Structures
Focuses on implementing algorithms efficiently and selecting appropriate data structures. Candidates should demonstrate correctness and numerical stability, analyze time and space complexity, choose memory efficient representations, exploit vectorization and batching, understand parallel and distributed implementations, use appropriate indexing or caching strategies, and optimize hotspots while preserving readability and testability.
Coding and Algorithmic Problem Solving
Assesses the candidate ability to solve algorithmic and data structure problems and produce correct, efficient code. Candidates should demonstrate clarifying questions, example driven reasoning, selection of appropriate data structures, analysis of time and space complexity, handling of edge cases and error conditions, and provision of test cases. Interviewers evaluate step by step problem solving, trade off discussion between approaches, code readability, and the ability to optimize and refactor solutions while explaining changes clearly.
Advanced Data Structures and Implementation
Deep and practical expertise in advanced data structures, their implementation details, performance characteristics, and selection for both algorithmic problems and production systems. Topics include arrays and dynamic arrays, strings, linked lists, stacks and queues, hash tables, heaps and priority queues, various tree forms including binary search trees and balanced trees, tries or prefix trees, segment trees and binary indexed trees or fenwick trees, union find or disjoint set union, suffix arrays, and advanced graph representations. Candidates should be able to implement core structures from first principles, demonstrate interfaces and invariants, reason about insertion deletion search traversal and iteration costs including worst case average case and amortized analysis, and discuss memory management and ownership in low level languages such as C and C plus plus as well as safe memory and reference use in managed languages. Evaluation also covers trade offs between contiguous and pointer based layouts, cache friendliness, concurrency considerations, selection of structures based on access patterns update frequency and memory constraints, handling of edge cases, testing and performance tuning for realistic inputs, and applying structures to problems such as top K queries prefix search connectivity range queries caches and union operations.
Python Data Structures and Algorithms
Core Python data structure and algorithm knowledge used for manipulating collections and solving common data processing problems. Candidates should know built in types such as lists, dictionaries, sets, and tuples and their performance characteristics; be able to implement and reason about searching, sorting, counting, deduplication, and frequency analysis tasks; and choose appropriate algorithms and data structures for time and space efficiency. Familiarity with Python standard library utilities such as collections.Counter, defaultdict, deque, and heapq is expected, as is writing Pythonic, clear code that handles edge cases. Questions may include algorithmic trade offs, complexity analysis, and applying these techniques to practical data manipulation problems where custom logic is required beyond what pandas or NumPy provide.