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').
Mobile Memory and Resource Management
Covers memory management principles and resource handling for mobile applications across platforms. Candidates should understand platform specific models such as automatic reference counting on iOS and garbage collection on Android, common causes of memory leaks and retain cycles, and how reference ownership and weak versus strong references affect lifetime. Include techniques for releasing resources correctly in lifecycle methods and avoiding long lived references that hold activity or context objects. Expect knowledge of memory profiling and diagnostics including tools and workflows for locating leaks and high memory usage, strategies to prevent out of memory conditions, and trade offs such as requesting a larger heap. Also cover cross platform considerations for frameworks like React Native and Flutter and practical practices for identifying and fixing real memory issues in production, such as analyzing heap dumps, using allocation instrumentation, and applying targeted fixes and regression tests.
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.
Error Handling and Defensive Programming
Covers designing and implementing defensive, fault tolerant code and system behaviors to prevent and mitigate production failures. Topics include input validation and sanitization, null and missing data handling, overflow and boundary protections, exception handling and propagation patterns, clear error reporting and structured logging for observability, graceful degradation and fallback strategies, retry and backoff policies and idempotency for safe retries. Also address concurrency and synchronization concerns, resource and memory management to avoid exhaustion, security related input checks, and how to document and escalate residual risks. Candidates should discuss pragmatic trade offs between robustness and complexity, show concrete defensive checks and assertions, and describe test strategies for error paths including unit tests and integration tests and how monitoring and operational responses tie into robustness.
Maintainability and Legacy Code
Covers strategies and principles for evolving codebases safely and keeping them easy to understand and change over time. Topics include design principles such as Single Responsibility, Open Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion, removing duplication, establishing appropriate abstraction boundaries, separation of concerns, identifying and remediating code smells, incremental refactoring approaches, regression risk mitigation via tests and feature toggles, backward compatibility and migration strategies, and prioritizing technical debt reduction. Interviewers assess the candidate ability to plan pragmatic refactors, minimize risk during change, and improve long term health of a codebase.
Programming Fundamentals and Code Quality
Encompasses core programming skills, data structures, basic algorithms, language fundamentals, and code quality practices. Expect proficiency with arrays, strings, lists, hash maps or dictionaries, sets, common collection operations, basic sorting and searching algorithms, and tradeoffs between data structures. Understand control flow, functions and modular design, classes and object oriented programming concepts including encapsulation, inheritance, and polymorphism, exception handling, file input and output, and common language idioms for mainstream interview languages such as Python, Java, and C plus plus. Emphasizes writing clean, readable, maintainable code: meaningful naming, modular functions, small interfaces, handling edge cases and errors, logging and documentation, simple testing and debugging strategies, and awareness of time and space complexity for common operations. Candidates should be able to implement correct solutions, follow language specific idioms where appropriate, and demonstrate attention to code quality and readability.
Object Oriented Design Principles
Technical principles for designing object oriented systems that are maintainable, extensible, and testable. Topics include single responsibility, open closed, Liskov substitution, interface segregation, and dependency inversion principles; composition versus inheritance trade offs; designing clear and minimal public interfaces and application programming interfaces; separation of concerns, low coupling and high cohesion; use of design patterns where appropriate; strategies for dependency management and dependency injection; and practices that improve testability such as designing for mocking and modularity. Interviewers evaluate ability to reason about class and module boundaries, extensibility, performance and complexity trade offs, and to produce clean code and APIs that support long term maintenance.
Arrays, Strings, and Linked Lists Mastery
Foundational data structures: arrays, strings, and linked lists. Covers core operations (insertion, deletion, traversal, searching), pattern usage, edge cases, and time/space complexity analysis, with a focus on practical implementation and common interview-style problems across mainstream programming languages.
Technical Background and Programming Skills
Clearly communicate your programming experience, including languages you're proficient in (C#, C++, JavaScript, Python). Mention any relevant coursework in data structures, algorithms, computer graphics, or physics simulation. Be honest about knowledge gaps while showing willingness to learn.
Haskell & Functional Programming Concepts
Haskell language fundamentals and functional programming concepts, including pure functions, immutability, higher-order functions, recursion, lazy evaluation, the type system and type classes, and core FP abstractions such as Functor, Applicative, and Monad (including monadic IO), along with common patterns and idioms used in Haskell programming.