Coding Interview Tips - LLM and AI & Language Model interview questions
At codinginterview.tips, our mission is to provide aspiring software engineers with the resources and guidance they need to excel in technical interviews at top tech companies, including FANG. We offer comprehensive advice on coding interviews, system design interviews, and other technical assessments, as well as tips on how to prepare effectively and present yourself confidently. Our goal is to help you land your dream job in the tech industry by providing you with the tools and knowledge you need to succeed.
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Coding interviews are an essential part of the hiring process for tech companies, especially FANG (Facebook, Amazon, Netflix, and Google). These interviews are designed to test a candidate's technical skills, problem-solving abilities, and coding proficiency. To pass these interviews, you need to have a solid understanding of computer science fundamentals, data structures, algorithms, and system design concepts. In this cheat sheet, we will cover everything you need to know to ace your coding interviews.
Computer Science Fundamentals
- Big O Notation
Big O notation is a mathematical notation used to describe the complexity of an algorithm. It is used to analyze the performance of an algorithm in terms of time and space complexity. The most common Big O notations are:
- O(1): constant time complexity
- O(log n): logarithmic time complexity
- O(n): linear time complexity
- O(n log n): linearithmic time complexity
- O(n^2): quadratic time complexity
- O(2^n): exponential time complexity
- Data Types
Data types are the building blocks of any programming language. The most common data types are:
- Integer: a whole number
- Float: a decimal number
- Boolean: true or false
- String: a sequence of characters
- Array: a collection of elements of the same data type
- Dictionary: a collection of key-value pairs
- Control Structures
Control structures are used to control the flow of a program. The most common control structures are:
- If-else statements: used to execute a block of code if a condition is true, and another block of code if the condition is false.
- Loops: used to repeat a block of code until a condition is met.
- Switch statements: used to execute different blocks of code based on different conditions.
Arrays are a collection of elements of the same data type. They are used to store and manipulate data in a structured way. The most common operations on arrays are:
- Accessing an element: accessing an element at a specific index.
- Inserting an element: inserting an element at a specific index.
- Deleting an element: deleting an element at a specific index.
- Searching for an element: searching for an element in the array.
- Linked Lists
Linked lists are a collection of nodes, where each node contains a value and a pointer to the next node. They are used to store and manipulate data in a dynamic way. The most common operations on linked lists are:
- Inserting a node: inserting a node at the beginning, end, or middle of the linked list.
- Deleting a node: deleting a node at the beginning, end, or middle of the linked list.
- Searching for a node: searching for a node with a specific value in the linked list.
Stacks are a collection of elements that follow the Last-In-First-Out (LIFO) principle. They are used to store and manipulate data in a structured way. The most common operations on stacks are:
- Pushing an element: adding an element to the top of the stack.
- Popping an element: removing the top element from the stack.
- Peeking at the top element: accessing the top element of the stack without removing it.
Queues are a collection of elements that follow the First-In-First-Out (FIFO) principle. They are used to store and manipulate data in a structured way. The most common operations on queues are:
- Enqueuing an element: adding an element to the end of the queue.
- Dequeuing an element: removing the first element from the queue.
- Peeking at the first element: accessing the first element of the queue without removing it.
- Sorting Algorithms
Sorting algorithms are used to sort a collection of elements in a specific order. The most common sorting algorithms are:
- Bubble sort: repeatedly swapping adjacent elements if they are in the wrong order.
- Selection sort: repeatedly selecting the smallest element and swapping it with the current element.
- Insertion sort: repeatedly inserting the current element into the correct position in the sorted subarray.
- Merge sort: recursively dividing the array into two halves, sorting them, and merging them back together.
- Quick sort: recursively partitioning the array into two subarrays, one with elements smaller than the pivot and one with elements larger than the pivot.
- Searching Algorithms
Searching algorithms are used to search for a specific element in a collection of elements. The most common searching algorithms are:
- Linear search: sequentially searching each element in the collection until the target element is found.
- Binary search: dividing the collection into two halves, discarding the half that does not contain the target element, and repeating the process until the target element is found.
Scalability is the ability of a system to handle increasing amounts of traffic or data without compromising performance or reliability. The most common techniques for achieving scalability are:
- Load balancing: distributing incoming traffic across multiple servers to prevent overload.
- Caching: storing frequently accessed data in memory to reduce database queries.
- Sharding: partitioning data across multiple servers to distribute the load.
- Replication: creating multiple copies of data to improve availability and reduce latency.
Availability is the ability of a system to remain operational and accessible to users at all times. The most common techniques for achieving availability are:
- Redundancy: creating multiple copies of critical components to ensure that the system can continue to function even if one component fails.
- Failover: automatically switching to a backup component if the primary component fails.
- Monitoring: continuously monitoring the system for potential issues and taking corrective action before they become critical.
Coding interviews can be challenging, but with the right preparation and practice, you can ace them. This cheat sheet covers the essential concepts, topics, and categories related to passing technical interviews at FANG and other tech companies. Remember to focus on computer science fundamentals, data structures, algorithms, and system design concepts, and practice solving coding problems regularly. Good luck!
Common Terms, Definitions and Jargon1. Algorithm - A set of instructions designed to solve a specific problem or perform a specific task.
2. Array - A collection of similar data types stored in contiguous memory locations.
3. Binary Search - A search algorithm that finds the position of a target value within a sorted array.
4. Big O Notation - A mathematical notation used to describe the complexity of an algorithm.
5. Binary Tree - A tree data structure in which each node has at most two children.
6. Bit Manipulation - The process of manipulating individual bits in a binary number.
7. Breadth-First Search - A search algorithm that explores all the vertices of a graph at the same level before moving on to the next level.
8. Bubble Sort - A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order.
9. Cache - A hardware or software component that stores data so that future requests for that data can be served faster.
10. Class - A blueprint for creating objects in object-oriented programming.
11. Closure - A function that has access to variables in its lexical scope, even when the function is executed outside that scope.
12. Compiler - A program that translates source code into machine code.
13. Concurrency - The ability of a program to execute multiple tasks simultaneously.
14. Constructor - A special method that is called when an object is created.
15. Data Structure - A way of organizing and storing data in a computer so that it can be accessed and used efficiently.
16. Depth-First Search - A search algorithm that explores as far as possible along each branch before backtracking.
17. Design Pattern - A general reusable solution to a commonly occurring problem in software design.
18. Divide and Conquer - A problem-solving strategy that involves breaking a problem into smaller sub-problems and solving them independently.
19. Dynamic Programming - A method for solving complex problems by breaking them down into simpler sub-problems and storing the solutions to those sub-problems.
20. Encapsulation - The practice of hiding the internal workings of an object from the outside world.
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