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.

40 questions

Network Programming and Scripting Proficiency

Show practical programming and automation skills focused on networking tasks, using languages such as Python, Go, or shell scripting. Topics include socket programming and protocol knowledge for TCP and UDP, HTTP and higher level APIs, automation of network devices and cloud platform APIs, use of client libraries and remote management protocols, writing maintainable scripts and tools with error handling and retries, testing and validation of network code, performance and scaling considerations for scripts and agents, debugging and logging strategies, and code review and collaboration practices for operational tooling. Candidates should be able to discuss examples of network automation, tooling, monitoring integrations, and how they handled reliability and performance trade offs.

40 questions

Debugging and Code Optimization

Practical debugging skills and techniques for improving code performance and complexity. Topics include tracing and reproducing bugs, stepping through execution, reasoning about time and space complexity, refactoring for performance, and applying algorithmic optimizations. Candidates should be able to demonstrate logical debugging approaches and make safe, measurable performance improvements to working code.

0 questions

Systems Programming & Low-Level Concepts

Systems programming concepts including memory management, pointers, memory layout, CPU architecture considerations, concurrency primitives, OS interactions, and performance optimization in low-level languages (C, C++). Covers how languages expose low-level resources, toolchains, and platform-specific behaviors; excludes high-level application development.

50 questions

Shell Scripting Basics

Practical ability to write and maintain simple shell scripts using Bash and other Unix shells to automate routine system and DevOps tasks. Core skills include variable assignment and substitution, control flow with conditionals such as if and else, looping constructs such as for and while, defining and invoking functions, basic error handling and exit code checks, and logging for observability. Candidates should also be comfortable with basic file and directory operations, invoking and composing commands, and text processing using tools such as grep, sed, and awk. Emphasis is placed on scripting best practices including input validation, defensive programming, clear code organization and readability, making scripts idempotent where appropriate, and handling common edge cases when automating system administration or deployment workflows.

40 questions

Python for Infrastructure Automation

Use the Python programming language to build automation for infrastructure tasks. Relevant skills include file input and output, parsing and emitting JSON and YAML configuration files, invoking and managing subprocesses, error handling and retries, working with common HTTP and SSH libraries for automation, and processing and transforming infrastructure data structures. Candidates should be able to demonstrate writing reliable scripts and small programs for provisioning, configuration, orchestration, and integrating with automation tooling and APIs.

40 questions

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.

41 questions

Backend Relevant Problem Domains

Practice problems involving strings, arrays, sorting, searching, trees, graphs, hash tables, and basic dynamic programming with a focus on real-world backend scenarios like rate limiting, caching, data validation, and routing.

40 questions

Automation Relevant Coding Problems

Practice problems involving: parsing and transforming log data, working with strings and regular expressions, counting and aggregating data, basic system operations like file handling and simple data processing, sorting and searching through configuration data. These often appear in SRE interviews to test automation-relevant thinking and real-world problem application.

40 questions

Api and Data Structure Design

Focuses on designing efficient data models and public interfaces that back real world backend features, and selecting or inventing data structures to meet functional and non functional requirements. Candidates should be able to design data structures and classes to support use cases such as rate limiters, leaderboards, session managers, cache layers, analytics counters, and queueing or aggregation services. Topics include mapping operations to data structure choices for latency and throughput goals, persistence and eviction strategies for cached state, concurrency control and atomic operations, synchronization or lock free designs, partitioning and sharding implications, and tradeoffs between in memory, persisted, and distributed storage. Also cover API design considerations such as endpoint and payload design, versioning, pagination, idempotency, error handling, security and authentication, monitoring and observability, and how API shape influences internal data models and query patterns. Interviewers may ask candidates to produce class or interface sketches, data schemas, and rationale for chosen approaches and complexity characteristics.

36 questions

Synchronization Primitives and Inter Task Communication

Master synchronization mechanisms: semaphores (binary, counting), mutexes, condition variables, and message queues. Understand when to use each primitive and their trade-offs. Discuss potential issues: priority inversion, deadlock, race conditions, and how to prevent them. Explain interrupt-safe synchronization. Discuss critical sections and atomic operations.

40 questions

Python Coding and Data Structures

Proficiency in Python, including arrays, dictionaries, linked lists, and basic algorithms. Ability to write efficient, clean code under time pressure. Understanding of time/space complexity and optimization.

0 questions

Python Automation and Monitoring

Focuses on using the Python programming language to automate operational tasks and implement monitoring workflows. Includes writing robust scripts and small applications for file input and output, subprocess management, making HTTP requests and handling responses, parsing and producing JSON, structured error handling and retries, logging and alerting, and clear code organization and packaging. Discuss when to choose Python over shell scripts for complexity, portability, error handling, and maintainability. Covers libraries and tooling for scheduling and background jobs, interacting with cloud provider software development kits, instrumenting applications for metrics and traces, integrating with monitoring stacks, and basic concurrency and asynchronous programming patterns when needed. Also includes testing and validation of automation scripts, secure handling of credentials and configuration, and deployment practices for operational scripts.

40 questions

Go

Go programming language fundamentals, including syntax, concurrency primitives (goroutines and channels), memory management concepts, standard library usage, and practical patterns for building infrastructure tooling and system utilities.

0 questions

Algorithm Implementation and Data Structures

Focuses on implementing algorithms correctly and efficiently, and choosing the right data structure for the problem. Candidates should analyze time and space complexity (Big-O), select data structures that balance correctness, memory footprint, and access patterns (arrays, hash maps, trees, heaps, graphs), handle edge cases and precision/overflow pitfalls where relevant, apply techniques such as caching, indexing, batching, or parallelization to remove performance hotspots, and balance optimization against code readability, maintainability, and testability.

0 questions

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.

0 questions

Concurrency and Multithreading

Principles and practical techniques for concurrent execution and safe access to shared state across threads or execution contexts. Covers synchronization primitives (locks, mutexes, semaphores, condition variables), atomic operations and memory ordering, avoiding deadlocks, race conditions, and livelocks, designing thread safe data structures, thread pools and work scheduling, and asynchronous or event driven execution models such as coroutines, futures and promises, and reactive streams. Also covers platform and language specific concurrency tools, for example java.util.concurrent and POSIX threads on backend systems, Grand Central Dispatch and OperationQueue on iOS, coroutines and structured concurrency on Android/Kotlin, and async/await models in other languages. Evaluations focus on reasoning about correctness under concurrent access, performance trade offs (throughput, latency, contention), and preventing blocking of latency sensitive execution paths (server request handlers, UI threads, game loops) to maintain responsiveness.

0 questions

Clean Code and Best Practices

Covers the principles and hands on practices that produce readable, maintainable, and reliable code. Core elements include intent revealing and consistent naming, small focused functions and classes that follow single responsibility, avoiding duplication through refactoring and appropriate abstractions, clear structure and separation of concerns, following language specific idioms and style guides, consistent formatting, concise comments that explain nonobvious intent, defensive programming and robust error handling, edge case handling and input validation, use of linters and static analysis, incremental refactoring techniques, and pragmatic trade offs between ideal design and delivery constraints. Interviewers will also probe involvement in code reviews, version control hygiene, code metrics, and how candidates advocate for and teach coding standards to peers.

40 questions

Medium Difficulty Coding Problems

Practice and master medium difficulty algorithmic coding problems that commonly appear in technical interviews. Topics include arrays, strings, linked lists, trees, graphs, hash tables, and dynamic programming. Typical techniques to know are two pointer methods, sliding window, breadth first search and depth first search, recursion and backtracking, memoization and bottom up dynamic programming, sorting and greedy heuristics, and common data structure operations. Interviewers evaluate systematic problem solving: clarifying requirements, designing a correct solution, explaining time and space complexity, handling edge cases and input validation, writing clean and working code in your chosen language, and then iterating to optimize performance. Candidates should be comfortable explaining tradeoffs between approaches, testing with example cases, and communicating thought process clearly while coding under time constraints.

40 questions

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.

40 questions

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.

40 questions

Python Fundamentals and Core Syntax

Comprehensive knowledge of core Python language features and syntax, including primitive and composite data types such as integer numbers, floating point numbers, strings, booleans, lists, dictionaries, sets, and tuples. Candidates should understand variable assignment and naming, operators for arithmetic, logical, and comparison operations, and control flow constructs including conditional statements and loops. Expect familiarity with function definition, invocation, parameter passing, return values, and scope rules, as well as common built in functions and idioms such as iteration utilities, list comprehensions, generator expressions, and basic functional utilities like map and filter. Candidates should demonstrate error and exception handling techniques and best practices for writing readable and maintainable code with modularization and clear naming. Practical skills include file input and output, working with common data formats such as comma separated values and JavaScript Object Notation, selecting appropriate data structures with attention to performance and memory characteristics, and applying memory efficient patterns for processing large data sets using iterators and generators. Familiarity with the standard library and common utilities for parsing and transforming data, and the ability to write small code snippets to solve algorithmic and data manipulation tasks, are expected.

0 questions

Core Data Structures

Fundamental built-in data structures used in everyday programming and coding interviews: arrays/lists, strings, and hash maps (dictionaries). For array-like sequences, cover indexing, slicing or sub-ranging, iteration, common mutation operations (appending, inserting, removing elements), common algorithms such as sorting and reversing, and the memory and performance implications of each. For strings, cover indexing, slicing or substring extraction, common operations such as splitting, joining, trimming, and replacing, and general approaches to string manipulation and pattern processing. For hash maps and dictionaries, cover key value semantics, insertion and lookup, iteration patterns, safe access idioms (defaults, existence checks), and using hash tables for counting and grouping. Candidates should know the time complexity of common operations in plain terms (constant time, linear time, quadratic time) and be able to choose the appropriate structure for a problem and reason about space and performance tradeoffs. Practice should include implementation level manipulation exercises and classic interview problems such as two sum and frequency counting, written clearly in whatever language the candidate is most comfortable with (a specific language may be used for concrete code examples without the underlying concept being tied to it).

40 questions

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.

40 questions

Backend Language Proficiency

Demonstrate strong practical knowledge of a backend programming language such as JavaScript with Node dot js, Python, Java, Go, or similar. Cover language fundamentals, idiomatic usage, standard library features, package and dependency management, common frameworks and ecosystem tools, testing strategies and tooling, error handling and observability, and patterns for maintainable server side code. Be ready to explain concurrency and asynchronous models in the language, performance considerations, security best practices, deployment and packaging approaches, and examples of backend services or APIs you built including trade offs and chosen libraries.

49 questions

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.

0 questions

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.

0 questions

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.

40 questions

Bash and Shell Scripting

Covers proficiency in writing reliable Bash and POSIX shell scripts to automate common Linux system administration and operational tasks. Topics include shell syntax, variables, parameter expansion, arrays, control flow such as conditionals and loops, functions and modular script design, input and output redirection and pipes, and use of core Unix utilities for text processing such as grep, sed, and awk. Emphasizes defensive and maintainable scripting practices including error handling, exit codes, trap usage, logging, input validation, command substitution, process and job management, debugging techniques, performance considerations, and secure handling of file and process permissions. Typical use cases include service management, backups, log parsing and rotation, user provisioning, monitoring checks, and small operational tooling.

40 questions

Python Data Types and Structures

Practical expertise with Python built in data types and collection types and how to use them idiomatically and efficiently. Topics include lists tuples dictionaries sets and related operations append extend pop sort comprehension and slicing, dictionary lookups and set operations, and utilities from the collections module. Candidates should know time and space characteristics of Python operations, how to manipulate these structures for algorithmic solutions, and how to write clear Pythonic implementations that leverage language features for performance and readability.

0 questions

Programming Fundamentals for Testing

Core programming knowledge as applied to writing reliable automated tests and test support code. Candidates should understand variables, conditionals, loops, functions, and basic data structures such as arrays and maps, and be able to use them to express test logic. Cover error handling, logging, basic concurrency considerations and how improper handling of asynchronous operations leads to flaky tests, plus common coding pitfalls in test code such as tight coupling, brittle locators, and duplicated setup. The focus is on practical coding skills that enable building, debugging, and maintaining test suites.

0 questions

Scripting and Automation Fundamentals

Practical scripting and basic programming skills used to build and maintain automation across development, testing, operations, and data workflows. Covers core language fundamentals in languages such as Python and shell/bash (variables, control flow, functions, and basic data structures), reading, modifying, and debugging small existing scripts, invoking system commands and working with subprocesses, basic regular expressions and text or log parsing, error handling and troubleshooting approaches, and designing small, repeatable utility scripts that automate repetitive tasks, process output, and support monitoring or reporting. Candidates are expected to write and troubleshoot short scripts and to reason about when automation is worth the investment.

0 questions

Programming Languages & Core Development Topics

Concurrency and Synchronization

Network Programming and Scripting Proficiency

Debugging and Code Optimization

Systems Programming & Low-Level Concepts

Shell Scripting Basics

Python for Infrastructure Automation

Error Handling and Defensive Programming

Backend Relevant Problem Domains

Automation Relevant Coding Problems

Api and Data Structure Design

Synchronization Primitives and Inter Task Communication

Python Coding and Data Structures

Python Automation and Monitoring

Go

Algorithm Implementation and Data Structures

Coding and Algorithmic Problem Solving

Concurrency and Multithreading

Clean Code and Best Practices

Medium Difficulty Coding Problems

Advanced Data Structures and Implementation

Programming Fundamentals and Code Quality

Python Fundamentals and Core Syntax

Core Data Structures

Error Handling and Code Quality

Backend Language Proficiency

Python Fundamentals and Problem Solving

Python Data Structures and Algorithms

Maintainability and Legacy Code

Bash and Shell Scripting

Python Data Types and Structures

Programming Fundamentals for Testing

Scripting and Automation Fundamentals