## 50+ array questions with solutions (easy, medium, hard)

To ace your coding interview for a software engineering job, you’ll need to understand arrays. They come up frequently in coding interviews and are fundamental to many other data structures too.

Let’s take a look at some array questions that come up in interviews.

6 typical array interview questions

- Given a sorted array, return the index of a given value, or -1 if the element cannot be found.
- What is the time complexity required to find if an array of sorted integers contains a given integer?
- Given an array with all integers between 1 and 100 except for one, find the missing number.
- Given a 2D array of integers, rotate clockwise without using additional memory.
- If you have two sorted arrays, how can you merge them and keep the resulting array sorted?
- Given unlimited coins in denominations of 1c, 2c, and 5c, how many different ways can you make a total of 20c? Can you solve the general version of this problem for an arbitrary target amount and a given list of denominations?

Below, we take a look at some more questions and provide you with links to high quality solutions to them. We explain how arrays work, their variations, and the most important things you need to know about them, including a useful 'cheat sheet' to remind you of the key points at a glance.

This is an overview of what we’ll cover:

- Easy array interview questions
- Medium array interview questions
- Hard array interview questions
- Array basics
- Array cheat sheet
- Mock interviews for software engineers

## Click here to practice coding interviews with ex-FAANG interviewers

1. easy array interview questions.

You might be tempted to try to read all of the possible questions and memorize the solutions, but this is not feasible. Interviewers will always try to find new questions, or ones that are not available online. Instead, you should use these questions to practice the fundamental concepts of arrays.

As you consider each question, try to replicate the conditions you’ll encounter in your interview. Begin by writing your own solution without external resources in a fixed amount of time.

If you get stuck, go ahead and look at the solutions, but then try the next one alone again. Don’t get stuck in a loop of reading as many solutions as possible! We’ve analysed dozens of questions and selected ones that are commonly asked and have clear and high-quality answers.

Here are some of the easiest questions you might get asked in a coding interview. These questions are often asked during the ‘phone screen’ stage, so you should be comfortable answering them without being able to write code or use a whiteboard.

## 1.1 Merge two sorted arrays

- Text guide (GeeksforGeeks)
- Video guide (TECH DOSE)

## 1.2 Remove duplicates from an array

- Video guide (Kevin Naughton Jr.)
- Text guide (W3Schools)
- Text guide (Javarevisted)
- Code example (LeetCode)

## 1.3 Count the frequency of an element in an array

- Video guide (SDET)

## 1.4 Two sum

- Text guide (Codeburst)

## 1.5 Find the minimum (or maximum) element of an array

- Text guide (Enjoy Algorithms)
- Text guide (After Academy)
- Video guide (GeeksforGeeks)

## 1.6 Remove duplicates from sorted array

- Text guide (Redquark)
- Video guide (Take u Forward)

## 1.7 Remove element in-place

- Video guide (Nick White)
- Code example (LeetCode)

## 1.8 Search Insert Position

- Text guide (Codesdope)
- Video guide (NeetCode)

## 1.9 Maximum Subarray

- Text guide (Wikipedia)
- Text guide (Techie Delight)
- Video guide (CS Dojo)

## 1.10 Plus One

- Text guide (Medium/Punitkmryh)
- Video guide (Back to Back SWE)

## 1.11 Convert Sorted Array to Binary Search Tree (Arrays/Binary Trees)

- Text guide (GeeksForGeeks)
- Video guide (Kevin Naughton Jr)

## 1.12 Single Number

- Text guide (Akhilpokle)

## 1.13 Count Primes

- Video guide (Terrible Whiteboard)

## 1.14 Contains Duplicate

1.15 third largest number, 1.16 count odd even.

- Text guide (W3resource)
- Video guide (Technotip)

## 2. Medium array interview questions

Here are some moderate-level questions that are often asked in a video call or onsite interview. You should be prepared to write code or sketch out the solutions on a whiteboard if asked.

## 2.1 Move all zeros to the beginning/end of an array

- Text guide (Educative)
- Video guide (Programming tutorials)

## 2.2 Find if a given element is in a sorted array (binary search)

- Text guide (Khan academy)
- Video guide (HackerRank)

## 2.3 Rotate an array

2.4 largest sum of non-adjacent numbers (dynamic programming).

- Text guide (Medium/Arun Kumar)
- Video guide (Coding Simplified)

## 2.5 A Product Array Puzzle

- Text guide (TutorialCup)

## 2.6 Maximum Product Subarray (Dynamic programming)

2.7 shortest unsorted continuous subarray.

- Text guide (Seanpgallivan)

## 2.8 Maximum sum of hour glass in matrix

- Video guide (Over The Shoulder Coding)

## 2.9 Paint House (Dynamic programming)

- Text guide (ProgrammerSought)

## 2.10 Minimum number of jumps to reach end

- Text guide (Medium/Himanshu)

## 2.11 Find duplicates in O(n) time and O(1) extra space

2.12 find three numbers with the maximum product.

- Video guide (Programmer Mitch)

## 2.13 Maximum Sum Circular Subarray

- Text Guide (Techie Delight)
- Video Guide (TECH DOSE)

## 2.14 Minimum number of swaps to sort an array

- Video guide (Brian Dyck)

## 3. Hard array interview questions

Similar to the moderate section, these more difficult questions may be asked in an onsite or video call interview. You will likely be given more time if you are expected to create a full solution.

## 3.1 Rotate a 2D array

- Text guide (Jack)
- Text guide (GeeksforGeeks)
- Video guide (Nick White)

## 3.2 Create change with coins (dynamic programming)

- Video guide (Back to Back SWE)

## 3.3 Sliding window maximum

- Video guide (Jessica Lin)

## 3.4 Find the smallest positive number missing from an unsorted array

- Text guide (Codes Dope)
- Video guide (Michael Muinos)

## 3.5 Find the missing number in unordered Arithmetic Progression

3.6 find the maximum j – i such that arr[j] > arr[i] (distance maximising problem).

- Video guide (Genetic Coders)

## 3.7 Array manipulation

- Text guide (The Poor Coder)

## 3.8 Median of Two Sorted Arrays

3.9 sudoku solver.

- Video guide (Back To Back SWE)

## 3.10 Largest Rectangle in Histogram

3.11 maximal rectangle in binary matrix, 3.12 find minimum in rotated sorted array .

- Text guide (Algorithmsandme)

## 3.13 Count of Smaller Numbers After Self

- Text guide (CodeStudio)
- Video guide (Happygirlzt)

## 3.14 Palindrome Pairs

3.15 sort an array containing 0’s, 1’s and 2’s.

- Text guide (Techie Delight)

## 3.16 Longest increasing subsequence

3.17 trapping rain water , 4. array basics.

In order to crack the questions above and others like them, you’ll need to have a strong understanding of arrays, how they work, and when to use them. Let’s get into it.

## 4.1 What is an array?

An array is a list-like data structure that contains a collection of values , each associated with a specific index , usually with a fixed overall size. For example, the image below shows an array that has space for up to nine elements, but contains only four. This array has the integers 1, 2, 3, and 4 as its values and these are at the “zeroth”, first, second, and third indices respectively.

Arrays are one of the most fundamental data structures in programming and computer science, and many more complex data structures are built using arrays. The array itself is not always as simple as it might seem, and it forms the basis for many tricky interview questions.

## 4.1.1 Types of arrays (Java, Python, C++)

Interviewers often ask questions about “arrays”, as if it cleanly refers to a single concept. In reality, there are different types of arrays, and different languages implement arrays in different ways, leading to some confusion and complexity. Mainstream programming languages offer a default built-in array implementation (e.g. `list` in Python, or `int []` in Java and C++), and usually offer alternative implementations that the user can import from a standard library.

In many languages, including Java, default arrays are static and homogenous. Static means that the size of the array (the number of elements that it can hold) has to be declared upfront, when the array is created. Homogenous means that all of the elements in the array must be of the same type - e.g. an array of integers cannot contain string or float elements.

In other languages, including Python, the default array (`list`) is dynamic and heterogeneous. This means that they can be resized dynamically at run time, and can contain a mix of different types.

You will also often encounter nested or multidimensional arrays (often called a matrix). For 2D arrays, you can usually think of these as tables with rows and columns.

Because array terminology and implementation differs across languages, it’s always a good idea to check your assumptions about a specific array question with your interviewer.

## 4.1.2 How arrays store data

As with strings, data stored in arrays is traditionally kept in the heap of computer memory. If you store a basic integer in a variable with a statement like `int x = 1;`, that value is stored on the stack. To answer many array-related interview questions, you should understand the fundamentals of stack vs heap .

Data in the heap has to be cleared manually in languages like C, or by the garbage collector in languages such as Java. You should be prepared to answer questions about the implications of this (for example, how it could lead to a memory leak ).

Because arrays need to store data in contiguous blocks of memory, the programmer often needs to be aware of tradeoffs around space and time when it comes to using arrays.

- If you don’t reserve enough space in your array, you waste time as you have to allocate a new array.
- If you reserve too much space, this is a waste of resources and could impact the requirements of your program, or other running programs.

Adding even a single element to a ‘full’ array is an expensive operation. A new (bigger) array has to be allocated, and every single element has to be copied across. Only then can the new element be added.

A common approach that languages use for dynamic arrays is to double their allocated size every time they become full. So if you need to add an 11th item to an array of size 10, the library will create a new array of size 20 and copy across the existing data.

This means that as you are adding elements to an array, most inserts will be fast, but your code will slow down significantly every time it triggers a resize.

## 4.1.3 How arrays compare to other data structures

Because strings are usually implemented as arrays of characters, many interview questions for arrays can be phrased as string interview questions, and vice-versa.

Arrays are also closely related to linked lists, and many questions will expect you to be able to explain the differences between them, and when one has an advantage over the other.

Finally, arrays are often contrasted with sets. When you want to get data at a specific index (e.g. “I need the fifth element in this list”), arrays perform better than sets, as you can access any given element by its index in O(1) time.

If you need to check if a specific value is contained in the array (“Does my array contain the value 5 at any position?”), arrays are not efficient. You need to loop through every single value to see if it matches what you are looking for, while sets can provide this in O(1) time.

Need a boost in your career as a software engineer?

If you want to improve your skills as an engineer or leader, tackle an issue at work, get promoted, or understand the next steps to take in your career, book a session with one of our software engineering coaches.

## 5. Array cheat sheet

You can download the cheat sheet here.

## 5.1 Related algorithms and techniques

- Binary search
- Dynamic Programming
- Converting to a Set
- Sliding window
- Two pointers
- Prefix sum
- Recursion
- Searching
- Looping
- Sorting

## 5.2 Related concepts

- Homogeneous (elements have same type)
- Dynamic (size can change)

## 5.3 Cheat sheet explained

The cheat sheet above is a summary of information you might need to know for an interview, but it’s usually not enough to simply memorize it. Instead, aim to understand each result so that you can give the answer in context.

The cheat sheet is broken into time complexity (the processing time for the various array operations) and space complexity (the amount of memory required). While you might be asked about these directly in relation to the array data structure, it’s more likely that you will need to know these in relation to specific array-related algorithms, such as searching and sorting, which is what the third section details.

For more information about time and space requirements of different algorithms, read our complete guide to big-O notation and complexity analysis .

## 5.3.1 Time complexity

For time complexity, some of the results are fairly intuitive. For example, accessing any element of an array is always O(1) as arrays are stored in contiguous memory, so accessing the 100th element is no harder than accessing the first one, and this is true for updating any specific element too.

Deleting or inserting an element can require us to touch every single other element in some cases, so this is O(n) in the worst case. For example, if we have an array of size 10 and we want to add an 11th element, we need to copy each element to a new array first, and then add the new one. However, this is rare, as we would usually double the size of the array every time we run out of space, making future inserts faster. Thus the amortized complexity is still constant as we can ‘pay off’ the expensive operation over time.

The time complexity is similar when searching for an element by value, where in the worst case we need to look at every single element before finding our target, but if we ‘get lucky’ we might find it in the first place we look (probably at the start of the array), so our best case is O(1).

## 5.3.2 Space complexity

In most cases, the space complexity of an array is simply the number of elements, so this is O(n). In some contexts, the array might be some (small) constant size, which means the space complexity is simplified to O(1). Space complexity is almost always only relevant in the context of a specific algorithm, which we cover in the next section.

## 5.3.3 Array algorithms complexity

We’ve listed the algorithms that interviewers will most frequently discuss while asking about arrays, but there are dozens of other search algorithms and sorting algorithms. One of the most important aspects to understand is the tradeoff between mergesort and quicksort. Quicksort works in place, so does not require additional memory, while Mergesort uses an auxiliary array, and therefore uses more space. On the flip side, the worst time complexity of mergesort is better than that of quicksort which can in some cases (e.g. when the array is already sorted) perform as badly as a naive bubble sort.

For the search algorithms, a key insight to understand is that binary search is log(n) as we can eliminate half of the array with each operation. Therefore doubling the size of the array requires only one more operation. By contrast, a linear search looks at every element until it finds the target, so doubling the size of the array also requires, on average, twice as many operations.

For example, searching an element using binary search in an array of one million elements needs a maximum of 20 comparisons. Doubling the array (two million elements) would only add one extra comparison (a total of 21 comparisons). By contrast, a linear search would need one million comparisons and doubling the array would also double the number of comparisons (to two million).

## 6. Mock interviews for software engineers

Before you start practicing interviews, you’ll want to make sure you have a strong understanding of not only linked lists but also the rest of the relevant data structures. Check out our guides for questions, explanations and helpful cheat sheets.

- Linked lists
- Stacks and Queues
- Coding interview examples (with solutions)

Once you’re confident on all the topics, you’ll want to start practicing answering coding questions in an interview situation.

One way of doing this is by practicing out loud, which is a very underrated way of preparing. However, sooner or later you’re probably going to want some expert interventions and feedback to really improve your interview skills.

That’s why we recommend practicing with ex-interviewers from top tech companies. If you know a software engineer who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice system design interviews 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today.

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Top 50 Array Problems with Solutions A handpicked selection of array-based coding problems for interview preparation, with documented solutions in popular languages. Boost your problem-solving skills and contribute to this open-source project. Happy coding!

## pranaydas1/Top-50-Array-Problems

Folders and files.

Name | Name | |||
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12 Commits | ||||

## Repository files navigation

Geeks for geeks - top 50 array problems.

Welcome to the "Geeks for Geeks - Top 50 Array Problems" repository! This collection includes problem statements and solutions for the "Top 50 Array Problems" from the Geeks for Geeks platform. These problems are a valuable resource for improving your data structures and algorithm skills and preparing for coding interviews.

## Visit the Website

https://practice.geeksforgeeks.org/explore?page=1&sprint=50746f92a895c22a50504ac0c1fb9c84&sortBy=submissions&sprint_name=Top%2050%20Array%20Problems

## Repository Overview

This repository is organized in a question-by-question format, with each problem statement and its corresponding solution available. Please note that these solutions are specifically designed for the Geeks for Geeks platform, as they rely on predefined input and test cases provided by Geeks for Geeks. As a result, they may not work in other code editors or environments.

Choose a Problem: Browse the repository to find a specific problem you'd like to practice.

Read the Problem Statement: Open the README file associated with the problem to read its description, constraints, and requirements.

Review the Solution: Explore the solution code provided in the accompanying code file. These solutions are tailored to work with Geeks for Geeks' predefined input and test cases.

Visit Geeks for Geeks: To practice and submit your solutions, visit the Geeks for Geeks platform ( https://www.geeksforgeeks.org/ ) and navigate to the respective problem. Use the provided solution in this repository as a reference to create your solution on the Geeks for Geeks platform.

Submit Your Solution: After solving the problem on the Geeks for Geeks platform, you can test it using their predefined test cases and submit your solution for evaluation.

## Contributions

This repository is open for contributions. If you have alternative solutions, improvements, or suggestions, feel free to create a pull request. Contributions are a valuable way to enhance the quality and variety of solutions available.

Please note that the solutions provided here are intended for learning and reference purposes, specifically for use on the Geeks for Geeks platform. They may not work in other coding environments due to the platform-specific input and test cases.

Happy coding and best of luck with your Geeks for Geeks practice!

## Array cheatsheet for coding interviews

Introduction β.

Arrays hold values of the same type at contiguous memory locations. In an array, we're usually concerned about two things - the position/index of an element and the element itself. Different programming languages implement arrays under the hood differently and can affect the time complexity of operations you make to the array. In some languages like Python, JavaScript, Ruby, PHP, the array (or list in Python) size is dynamic and you do not need to have a size defined beforehand when creating the array. As a result, people usually have an easier time using these languages for interviews.

Arrays are among the most common data structures encountered during interviews. Questions which ask about other topics would likely involve arrays/sequences as well. Mastery of array is essential for interviews!

- Store multiple elements of the same type with one single variable name
- Accessing elements is fast as long as you have the index, as opposed to linked lists where you have to traverse from the head.

Disadvantages

- Addition and removal of elements into/from the middle of an array is slow because the remaining elements need to be shifted to accommodate the new/missing element. An exception to this is if the position to be inserted/removed is at the end of the array.
- For certain languages where the array size is fixed, it cannot alter its size after initialization. If an insertion causes the total number of elements to exceed the size, a new array has to be allocated and the existing elements have to be copied over. The act of creating a new array and transferring elements over takes O(n) time.

## Learning resources β

- Array in Data Structure: What is, Arrays Operations , Guru99
- Arrays , University of California San Diego

## Common terms β

Common terms you see when doing problems involving arrays:

- Example: given an array [2, 3, 6, 1, 5, 4] , [3, 6, 1] is a subarray while [3, 1, 5] is not a subarray.
- Example: given an array [2, 3, 6, 1, 5, 4] , [3, 1, 5] is a subsequence but [3, 5, 1] is not a subsequence.

## Time complexity β

Operation | Big-O | Note |
---|---|---|

Access | O(1) | |

Search | O(n) | |

Search (sorted array) | O(log(n)) | |

Insert | O(n) | Insertion would require shifting all the subsequent elements to the right by one and that takes O(n) |

Insert (at the end) | O(1) | Special case of insertion where no other element needs to be shifted |

Remove | O(n) | Removal would require shifting all the subsequent elements to the left by one and that takes O(n) |

Remove (at the end) | O(1) | Special case of removal where no other element needs to be shifted |

## Things to look out for during interviews β

- Clarify if there are duplicate values in the array. Would the presence of duplicate values affect the answer? Does it make the question simpler or harder?
- When using an index to iterate through array elements, be careful not to go out of bounds.
- Be mindful about slicing or concatenating arrays in your code. Typically, slicing and concatenating arrays would take O(n) time. Use start and end indices to demarcate a subarray/range where possible.

## Corner cases β

- Empty sequence
- Sequence with 1 or 2 elements
- Sequence with repeated elements
- Duplicated values in the sequence

## Techniques β

Note that because both arrays and strings are sequences (a string is an array of characters), most of the techniques here will apply to string problems.

## Sliding window β

Master the sliding window technique that applies to many subarray/substring problems. In a sliding window, the two pointers usually move in the same direction will never overtake each other. This ensures that each value is only visited at most twice and the time complexity is still O(n). Examples: Longest Substring Without Repeating Characters , Minimum Size Subarray Sum , Minimum Window Substring

## Two pointers β

Two pointers is a more general version of sliding window where the pointers can cross each other and can be on different arrays. Examples: Sort Colors , Palindromic Substrings

When you are given two arrays to process, it is common to have one index per array (pointer) to traverse/compare the both of them, incrementing one of the pointers when relevant. For example, we use this approach to merge two sorted arrays. Examples: Merge Sorted Array

## Traversing from the right β

Sometimes you can traverse the array starting from the right instead of the conventional approach of from the left. Examples: Daily Temperatures , Number of Visible People in a Queue

## Sorting the array β

Is the array sorted or partially sorted? If it is, some form of binary search should be possible. This also usually means that the interviewer is looking for a solution that is faster than O(n).

Can you sort the array? Sometimes sorting the array first may significantly simplify the problem. Obviously this would not work if the order of array elements need to be preserved. Examples: Merge Intervals , Non-overlapping Intervals

## Precomputation β

For questions where summation or multiplication of a subarray is involved, pre-computation using hashing or a prefix/suffix sum/product might be useful. Examples: Product of Array Except Self , Minimum Size Subarray Sum , LeetCode questions tagged "prefix-sum"

## Index as a hash key β

If you are given a sequence and the interviewer asks for O(1) space, it might be possible to use the array itself as a hash table. For example, if the array only has values from 1 to N, where N is the length of the array, negate the value at that index (minus one) to indicate presence of that number. Examples: First Missing Positive , Daily Temperatures

## Traversing the array more than once β

This might be obvious, but traversing the array twice/thrice (as long as fewer than n times) is still O(n). Sometimes traversing the array more than once can help you solve the problem while keeping the time complexity to O(n).

## Essential questions β

These are essential questions to practice if you're studying for this topic.

- Best Time to Buy and Sell Stock
- Product of Array Except Self
- Maximum Subarray

## Recommended practice questions β

These are recommended questions to practice after you have studied for the topic and have practiced the essential questions.

- Contains Duplicate
- Maximum Product Subarray
- Search in Rotated Sorted Array
- Container With Most Water
- Sliding Window Maximum

## Recommended courses β

Algomonster β.

AlgoMonster aims to help you ace the technical interview in the shortest time possible . By Google engineers, AlgoMonster uses a data-driven approach to teach you the most useful key question patterns and has contents to help you quickly revise basic data structures and algorithms. Best of all, AlgoMonster is not subscription-based - pay a one-time fee and get lifetime access . Join today for a 70% discount β

## Grokking the Coding Interview: Patterns for Coding Questions β

This course on by Design Gurus expands upon the questions on the recommended practice questions but approaches the practicing from a questions pattern perspective, which is an approach I also agree with for learning and have personally used to get better at coding interviews. The course allows you to practice selected questions in Java, Python, C++, JavaScript and also provides sample solutions in those languages along with step-by-step visualizations. Learn and understand patterns, not memorize answers! Get lifetime access now β

## Master the Coding Interview: Data Structures + Algorithms β

This Udemy bestseller is one of the highest-rated interview preparation course (4.6 stars, 21.5k ratings, 135k students) and packs 19 hours worth of contents into it. Like Tech Interview Handbook, it goes beyond coding interviews and covers resume, non-technical interviews, negotiations. It's an all-in-one package! Note that JavaScript is being used for the coding demos. Check it out β

## Table of Contents

- Introduction
- Learning resources
- Common terms
- Time complexity
- Things to look out for during interviews
- Corner cases
- Essential questions
- Recommended practice questions
- Recommended courses

- Interview Preparation Kit

## 2D Array - DS Easy Problem Solving (Basic) Max Score: 15 Success Rate: 93.16%

Arrays: left rotation easy problem solving (basic) max score: 20 success rate: 91.23%, new year chaos medium problem solving (basic) max score: 40 success rate: 67.55%, minimum swaps 2 medium problem solving (intermediate) max score: 40 success rate: 77.94%, array manipulation hard problem solving (intermediate) max score: 60 success rate: 61.54%, cookie support is required to access hackerrank.

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## DEV Community

Posted on Mar 23, 2019 • Updated on Jan 28, 2023

## 50+ Data Structure and Algorithms Problems from Coding Interviews

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

There are a lot of computer science graduates and programmers applying for programming, coding, and software development roles at startups like Uber and Netflix; big organizations like Amazon , Microsoft , and Google ; and service-based companies like Infosys or Luxsoft, but many of them have no idea of what kind of programming interview questions to expect when you're applying for a job with these companies.

In this article, I'll share some frequently asked programming interview questions from different Job interviews for programmers at different levels of experience,from people who have just graduated from college to programmers with one to two years of experience.

Coding interviews are comprised mainly of d ata structure and algorithm-based questions as well as some of the logical questions such as, How do you swap two integers without using a temporary variable?

I think it's helpful to divide coding interview questions into different topic areas.

The topic areas I've seen most often in interviews are array , linked list , string , binary tree , as well as questions from algorithms like string algorithm, sorting algorithms like quicksort or radix sort , and other miscellaneous ones), and that's what you will find in this article.

It's not guaranteed that you will be asked these coding or data structure and algorithmic questions, but they will give you enough of an idea of the kinds of questions you can expect in a real programming job interview.

Once you have gone through these questions, you should feel confident enough to attend any telephonic or face-to-face interviews.

Btw, there is no point in attempting these questions if you don't have sufficient knowledge of essential Data Structure and Algorithms or you have not touched them for ages.

In that case, you should take a good introductory course like Data Structures and Algorithms: Deep Dive Using Java to refresh your DS and algorithms skills.

## Top 50 Algorithms and Coding Interview Questions

Without any further ado, here is my list of some of the most frequently asked coding interview questions from programming job interviews :

## 1. Array Coding Interview Questions

An array is the most fundamental data structure, which stores elements at a contiguous memory location. It is also one of the darling topics of interviewers and you will hear a lot of questions about an array in any coding interview , like reversing an array, sorting the array, or searching elements on the array.

The key benefit of an array data structure is that it offers fast O(1) search if you know the index, but adding and removing an element from an array is slow because you cannot change the size of the array once it's created.

In order to create a shorter or longer array, you need to create a new array and copy all elements from old to new.

The key to solving array-based questions is having a good knowledge of array data structure as well as basic programming constructors such as loop, recursion, and fundamental operators.

Here are some tips to solve array based coding problems:

- array index starts at zero
- You can use loops to iterate over array
- array elements are stored in contiguous memory location so you can also access them using pointer arithmetic
- Array provides O(1) performance for search using index
- Adding or removing elements are slower in array due to re-sizing

Here are some of the popular array-based coding interview questions for your practice:

- How do you find the missing number in a given integer array of 1 to 100 ? ( solution )
- How do you find the duplicate number on a given integer array? ( solution )
- How do you find the largest and smallest number in an unsorted integer array? ( solution )
- How do you find all pairs of an integer array whose sum is equal to a given number?( solution )
- How do you find duplicate numbers in an array if it contains multiple duplicates?( solution )
- How are duplicates removed from a given array in Java? ( solution )
- How is an integer array sorted in place using the quicksort algorithm? ( solution )
- How do you remove duplicates from an array in place? ( solution )
- How do you reverse an array in place in Java? ( solution )
- How are duplicates removed from an array without using any library? ( solution )

These questions will not only help you to develop your problem-solving skills but also improve your knowledge of the array data structure.

If you need more advanced questions based upon array then you can see also see The Coding Interview Bootcamp: Algorithms + Data Structures , a Bootcamp style course on algorithms, especially designed for interview preparation to get a job on technical giants like Google, Microsoft, Apple, Facebook, etc.

And, if you feel 10 is not enough questions and you need more practice, then you can also check out this list of 30 array questions .

## 2. Linked List Programming Interview Questions

A linked list is another common data structure that complements the array data structure. Similar to the array, it is also a linear data structure and stores elements in a linear fashion.

However, unlike the array, it doesn't store them in contiguous locations; instead, they are scattered everywhere in memory, which is connected to each other using nodes.

A linked list is nothing but a list of nodes where each node contains the value stored and the address of the next node.

Because of this structure, it's easy to add and remove elements in a linked list , as you just need to change the link instead of creating the array, but the search is difficult and often requires O(n) time to find an element in the singly linked list.

This article provides more information on the difference between an array and linked list data structures.

It also comes in varieties like a singly linked list, which allows you to traverse in one direction (forward or reverse); a doubly linked list , which allows you to traverse in both directions (forward and backward); and finally, the circular linked list, which forms a circle.

In order to solve linked list-based questions, a good knowledge of recursion is important, because a linked list is a recursive data structure .

If you take one node from a linked list, the remaining data structure is still a linked list, and because of that, many linked list problems have simpler recursive solutions than iterative ones.

Here are some of the most common and popular linked list interview questions and their solutions:

- How do you find the middle element of a singly linked list in one pass? ( solution )
- How do you check if a given linked list contains a cycle? How do you find the starting node of the cycle? ( solution )
- How do you reverse a linked list? ( solution )
- How do you reverse a singly linked list without recursion? ( solution )
- How are duplicate nodes removed in an unsorted linked list? ( solution )
- How do you find the length of a singly linked list? ( solution )
- How do you find the third node from the end in a singly linked list? ( solution )
- How do you find the sum of two linked lists using Stack? ( solution )

These questions will help you to develop your problem-solving skills as well as improve your knowledge of the linked list data structure.

If you are having trouble solving these linked list coding questions then I suggest you refresh your data structure and algorithms skill by going through Data Structures and Algorithms: Deep Dive ** Using Java** course.

You can also check out this list of 30 linked list interview questions for more practice questions.

## 3. String Coding Interview Questions

Along with array and linked list data structures, a string is another popular topic on programming job interviews. I have never participated in a coding interview where no string-based questions were asked.

A good thing about the string is that if you know the array, you can solve string-based questions easily because strings are nothing but a character array .

So all the techniques you learn by solving array-based coding questions can be used to solve string programming questions as well.

Here is my list of frequently asked string coding questions from programming job interviews:

- How do you print duplicate characters from a string? ( solution )
- How do you check if two strings are anagrams of each other? ( solution )
- How do you print the first non-repeated character from a string? ( solution )
- How can a given string be reversed using recursion? ( solution )
- How do you check if a string contains only digits? ( solution )
- How are duplicate characters found in a string? ( solution )
- How do you count a number of vowels and consonants in a given string? ( solution )
- How do you count the occurrence of a given character in a string? ( solution )
- How do you find all permutations of a string? ( solution )
- How do you reverse words in a given sentence without using any library method? ( solution )
- How do you check if two strings are a rotation of each other? ( solution )
- How do you check if a given string is a palindrome? ( solution )

These questions help improve your knowledge of string as a data structure. If you can solve all these String questions without any help then you are in good shape.

For more advanced questions, I suggest you solve problems given in the Algorithm Design Manual by Steven Skiena , a book with the toughest algorithm questions.

If you need more practice, here is another list of 20 string coding questions .

## 4. Binary Tree Coding Interview Questions

So far, we have looked at only the linear data structure, but all information in the real world cannot be represented in a linear fashion, and that's where tree data structure helps.

The tree data structure is a data structure that allows you to store your data in a hierarchical fashion. Depending on how you store data, there are different types of trees, such as a binary tree , where each node has, at most, two child nodes.

Along with its close cousin binary search tree , it's also one of the most popular tree data structures. Therefore, you will find a lot of questions based on them, such as how to traverse them, count nodes, find depth, and check if they are balanced or not.

A key point to solving binary tree questions is a strong knowledge of theory, like what is the size or depth of the binary tree, what is a leaf, and what is a node, as well as an understanding of the popular traversing algorithms, like pre-, post-, and in-order traversal.

Here is a list of popular binary tree-based coding questions from software engineer or developer job interviews:

- How is a binary search tree implemented? ( solution )
- How do you perform preorder traversal in a given binary tree?( solution )
- How do you traverse a given binary tree in preorder without recursion?( solution )
- How do you perform an inorder traversal in a given binary tree?*( solution )
- How do you print all nodes of a given binary tree using inorder traversal without recursion? ( solution )
- How do you implement a postorder traversal algorithm? ( solution )
- How do you traverse a binary tree in postorder traversal without recursion?( solution )
- How are all leaves of a binary search tree printed?( solution )
- How do you count a number of leaf nodes in a given binary tree?( solution )
- How do you perform a binary search in a given array?( solution )

If you feel that your understanding of binary tree coding is inadequate and you can't solve these questions on your own, I suggest you go back and pick a good data structure and algorithm course like From 0 to 1: Data Structures & Algorithms in Java .

If you need some more recommendations, here is my list of useful data structure algorithm books and courses to start with.

## 5. Miscellaneous Coding Interview Questions

Apart from data structure-based questions, most of the programming job interviews also ask algorithms , software design , bit manipulation, and general logic-based questions, which I'll describe in this section.

It's important that you practice these concepts because sometimes they become tricky to solve in the actual interview. Having practiced them before not only makes you familiar with them but also gives you more confidence in explaining the solution to the interviewer.

- How is a bubble sort algorithm implemented? ( solution )
- How is an iterative quicksort algorithm implemented? ( solution )
- How do you implement an insertion sort algorithm? ( solution )
- How is a merge sort algorithm implemented? ( solution )
- How do you implement a bucket sort algorithm?( solution )
- How do you implement a counting sort algorithm?( solution )
- How is a radix sort algorithm implemented?( solution )
- How do you swap two numbers without using the third variable? ( solution )
- How do you check if two rectangles overlap with each other? ( solution )
- How do you design a vending machine? ( solution )

If you need more such coding questions you can take help from books like Cracking The Code Interview , by Gayle Laakmann McDowell which presents 189+ Programming questions and solution. A good book to prepare for programming job interviews in a short time.

By the way, the more questions you solve in practice, the better your preparation will be. So, if you think 50 is not enough and you need more, then check out these additional 50 programming questions for telephone interviews and these books and courses for more thorough preparation.

## Now You're Ready for the Coding Interview

These are some of the most common questions outside of data structure and algorithms that help you to do really well in your interview.

I have also shared a lot of these questions on my blog , so if you are really interested, you can always go there and search for them.

These common coding, data structure, and algorithm questions are the ones you need to know to successfully interview with any company, big or small, for any level of programming job.

If you are looking for a programming or software development job, you can start your preparation with this list of coding questions.

This list provides good topics to prepare and also helps assess your preparation to find out your areas of strength and weakness.

Good knowledge of data structure and algorithms is important for success in coding interviews and that's where you should focus most of your attention.

Further Learning Data Structures and Algorithms: Deep Dive Using Java Master the Coding Interview: Data Structures + Algorithms by Andrei Negaoie Grokking the Coding Interview: Patterns for Coding Questions Algorithms and Data Structures - Part 1 and 2 10 Books to Prepare Technical Programming/Coding Job Interviews 10 Algorithm Books Every Programmer Should Read Top 5 Data Structure and Algorithm Books for Java Developers From 0 to 1: Data Structures & Algorithms in Java Data Structure and Algorithms Analysisβ---βJob Interview

## Closing Notes

Thanks, You made it to the end of the article ... Good luck with your programming interview! It's certainly not going to be easy, but by following this roadmap and guide, you are one step closer to becoming a DevOps engineer .

If you like this article, then please share it with your friends and colleagues, and don't forget to follow javinpaul on Twitter!

## P.S.β---βIf you need some FREE resources, you can check out this list of free data structure and algorithm courses to start your preparation.

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## Top 50 Array Interview Questions and Answers

Array Interview Questions and Answers" provides concise, insightful responses to common array-related queries, essential for aspiring programmers and interview preparation.

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The comprehensive guide of Array Interview Questions and Answers is designed to help you navigate and excel in array-related interviews.

Array manipulation is a fundamental skill for coding interviews, and mastering these questions is crucial for success in technical assessments. This compilation covers a diverse range of array-related topics whether you're a beginner looking to solidify your understanding or an experienced coder aiming to polish your skills.

Dive into these Array interview questions and answers to sharpen your problem-solving abilities and boost your confidence in handling array challenges during interviews!Β

## Array Interview Questions for Freshers

Preparing for an array interview as a fresher is crucial for securing a strong foundation in programming. Array-related questions serve as a litmus test for problem-solving skills and algorithmic understanding.

Below are key Array interview questions for freshers are tailored to assess their proficiency in arrays, offering valuable insights into their coding aptitude and logical reasoning. Brush up on these concepts, practice diligently, and approach interviews with confidence to make a lasting impression on potential employers.

## What is an array and how is it used in programming languages?

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An array is a data structure in programming languages , providing a systematic way to store and organize elements of the same data type. It enables efficient storage and retrieval of values by utilizing a contiguous memory block.

Arrays are crucial for tasks such as iteration, sorting, and searching, enhancing the overall efficiency of algorithms. They serve as a fundamental building block for various data manipulation operations in programming.

## How do you initialize an array in languages like Java , C++ , or Python?

Initializing an array in Java involves specifying the data type and using the new keyword, followed by the array type and size.

Declare the array type and size in C++ within square brackets.

Use a list to represent an array in Python , specifying the elements within square brackets.

## Can you explain the difference between a one-dimensional and a two-dimensional array?

A one-dimensional array is a linear collection of elements, arranged sequentially in a single row, accessed by a single index. A two-dimensional array in contrast, is organized as a grid with rows and columns, requiring two indices to access a specific element.

The primary distinction lies in dimensionality, where one-dimensional arrays are unidimensional, and two-dimensional arrays are bidimensional.

## What is the significance of array indexes, and how are they used?

The significance of array indexes lies in their role as numerical identifiers for elements within an array. These indexes start from zero and help locate specific data points within the array.Β

Efficient manipulation and retrieval of data are facilitated by these numerical references. Array indexes in programming languages are crucial for performing operations like insertion, deletion, and updating elements.

## How do you access a specific element in an array?

Use array index to access a specific element in an array. Indexing in most programming languages starts from 0, so the first element is accessed with index 0, the second with index 1, and so on. For example, in Python, accessing an element in an array named 'arr' using square brackets like this: arr[2], retrieves the third element.Β

Keep in mind that exceeding the array bounds lead to errors, so it's essential to ensure the index is within the valid range.

## What is a multi-dimensional array, and can you give an example of its use?

A multi-dimensional array is an array with more than one dimension, allowing storage of data in multiple levels. This structure is useful for representing tables, matrices, or other complex data sets. E011ach element stores information about a specific square, utilizing two dimensions to represent rows and columns efficiently.

## How do you iterate through an array using a loop?

Employ a variety of loop constructs in programming languages such as for loops, while loops, or do-while loops to iterate through an array using a loop. These loops allow you to sequentially access each element in the array, performing operations or checks until the entire array has been traversed. Interact with each element individually utilizing the loop index or pointer, facilitating tasks like data manipulation, searching, or sorting within the array.

## What are the advantages of using arrays over other data structures in certain scenarios?

Arrays offer advantages over alternative data structures due to their contiguous memory allocation and constant-time access capabilities. This compact memory arrangement allows for efficient indexing, resulting in swift retrieval and manipulation of elements.Β

Arrays are well-suited for scenarios requiring fixed-size collections, ensuring predictable resource usage. This characteristic is valuable in applications where space constraints or predefined data structures are essential. Arrays exhibit straightforward implementation and ease of use, simplifying code and promoting readability. This simplicity makes arrays an optimal choice for scenarios where a balance between performance and simplicity is crucial.

## Can you explain the concept of dynamic arrays?

Dynamic arrays, also known as resizable arrays or ArrayLists in certain programming languages, are data structures that allow flexible resizing during runtime. Dynamic arrays dynamically adjust their size to accommodate varying amounts of data. This is achieved by allocating memory as needed and copying the existing elements to the newly allocated space.

Dynamic arrays offer efficient random access and are used due to their ability to dynamically adapt to changing storage requirements in real-time.

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## What is the difference between statically-typed and dynamically-typed languages when dealing with arrays?

Statically-typed languages require declaring the data type of an array at compile-time, providing strict type checking. Dynamically-typed languages, in contrast, determine the array's type at runtime, offering more flexibility but potentially leading to runtime errors. The choice between them impacts code efficiency, type safety, and development speed.

## How do you add elements to an array, and what are the limitations?

The "push" method is used in most programming languages to append items at the end for adding elements in an array. Alternatively, you can also assign values directly to specific indices in an array. It's crucial to be mindful of the array's size limitations, as exceeding its predefined capacity leads to memory overflow or runtime errors. Carefully manage the array's length to avoid performance issues and ensure optimal program execution.

## What is array slicing and how is it useful?

Array slicing is the process of extracting a portion of an array in a concise manner. It allows selecting specific elements based on their indices or ranges. This feature is beneficial for efficiently manipulating and extracting subsets of data within an array, streamlining operations and enhancing code readability.

## How do you remove elements from an array, and what happens to the array size?

Use methods like splice() in JavaScript or remove() in Python to remove elements from an array. These functions allow you to specify the index or value to be removed.

The array size is adjusted automatically upon removing elements. The size decreases by the number of elements removed, ensuring the array remains contiguous.

## Can you explain the concept of sparse arrays?

A sparse array is a data structure that efficiently represents arrays where the majority of elements have a default value. Sparse arrays store only non-default values, reducing storage space and computational overhead. This is useful when dealing with large datasets containing mostly identical or zero values.Β

Sparse arrays employ techniques like dictionaries or linked lists to store and access non-default elements, optimizing memory usage in scenarios where conventional arrays would be inefficient.

## What are some common array operations in programming languages?

Some common array operations in programming languages include accessing elements, inserting values, deleting elements, and updating values.

Accessing an array element is done using its index, starting from 0. Inserting values involves adding elements at specific positions or appending at the end. Deleting elements removes them from the array, while updating involves modifying existing values.

Other operations include searching for elements, sorting the array, and finding the length or size of the array. These operations form the foundation for efficient manipulation of arrays in programming.

## How do you reverse an array and what is the significance of doing so?

Iterate through half of the array length and swap elements from the beginning with those from the end to reverse an array.

This process is significant for various reasons in programming. It's a common algorithmic exercise that tests understanding of array manipulation. Reversing arrays is also practical for tasks like palindrome checking or optimizing certain algorithms. Additionally, reversing arrays is crucial in scenarios where data needs to be presented in the opposite order for better user experience or analysis.

## What is the role of memory allocation in array management?

The role of memory allocation in array management is to determine the storage space assigned to an array. The allocated memory ensures that the array has sufficient contiguous space to store elements efficiently. Proper memory allocation allows for easy access and manipulation of array elements during program execution.

Additionally, it influences the overall performance of algorithms and operations involving the array. Efficient memory allocation optimizes the utilization of available resources, contributing to the effectiveness of array management.

## Can you demonstrate how to sort an array using a basic algorithm?

Sorting an array is efficiently achieved through the Bubble Sort algorithm. This straightforward approach repeatedly iterates through the array, swapping adjacent elements if they are in the wrong order.

Here's a simple Python code snippet for a basic array sorting using Bubble Sort:

## How do arrays interact with functions or methods in a program?

Arrays interact with functions or methods in a program through parameters and return values. The function receives a reference to the array when passing an array as a parameter, allowing it to modify the original data.

Functions also return arrays, providing a convenient way to encapsulate logic and data. Moreover, array methods, such as map, filter, and reduce, offer concise ways to manipulate arrays within functions. These methods take a function as an argument, applying it to each element of the array.

## What are some common errors or issues encountered when working with arrays, and how can they be resolved?

Some common errors or issues encountered when working with arrays are discussed below along with their resolutions.

- Index Out of Bounds:

Issue: Accessing an array element with an index outside its bounds.

Resolution: Ensure index values are within the array's valid range.

- Null or Undefined Elements:

Issue: Operating on uninitialized or null elements in the array.

Resolution: Initialize array elements or handle null checks appropriately.

- Memory Overflows:

Issue: Allocating insufficient memory for array storage.

Resolution: Dynamically allocate memory or use data structures with dynamic sizing.

- Incorrect Data Types:

Issue: Storing incompatible data types within the array.

Resolution: Use consistent data types or implement type checking before operations.

- Inefficient Search Operations:

Issue: Inefficient search algorithms leading to slow performance.

Resolution: Opt for efficient search algorithms like binary search for sorted arrays.

- Unintended Mutations:

Issue: Modifying array elements unintentionally.

Resolution: Be cautious with in-place operations and consider creating a new array when needed.

- Unoptimized Loops:

Issue: Inefficient loop structures affecting array iteration.

Resolution: Optimize loops for better performance, minimizing unnecessary operations.

- Sparse Arrays:

Issue: Arrays with a significant number of unassigned or empty slots.

Resolution: Consider alternative data structures like hash tables for sparse data.

- Inadequate Error Handling:

Issue: Insufficient error handling for array-related operations.

Resolution: Implement robust error-handling mechanisms to catch and address issues.

- Multi-dimensional Array Confusion:

Issue: Confusion or errors when working with multi-dimensional arrays.

Resolution: Clearly understand and manage indices for each dimension to avoid confusion.

## How Do You Implement an Array Data Structure From Scratch in a Programming Language That Does Not Provide Built-in Array Support?

Create a class or structure with properties for size and elements to implement an array data structure from scratch in a programming language lacking built-in array support. Define methods for initialization, access, insertion, and deletion operations. Use a dynamic memory allocation mechanism for flexibility. Here's a basic example in Python:

This example demonstrates a simple array implementation with methods for basic operations, maintaining bounds checks.

## What are the time complexities of various array operations, and how can they be optimized?

The time complexities of various array operations are crucial considerations for optimizing performance.

- Accessing an element by index:

Time Complexity: O(1)

Optimization: No further optimization possible; constant time complexity.

- Inserting/deleting at the beginning:

Time Complexity: O(n)

Optimization: Use data structures like linked lists for constant-time insertions/deletions.

- Inserting/deleting at the end:

Time Complexity: O(1) for inserting, O(n) for deleting (due to shifting).

Optimization: Consider using a dynamic array with occasional resizing for deletions.

- Inserting/deleting in the middle:

Optimization: Use a data structure with better middle insertion/deletion performance, like a linked list.

- Searching for an element:

Optimization: Implement binary search if the array is sorted to achieve O(log n) complexity.

- Sorting the array:

Time Complexity: O(n log n) for efficient algorithms like Merge Sort or QuickSort.

Optimization: Choose the appropriate sorting algorithm based on specific requirements.

- Merging two arrays:

Optimization: Ensure sufficient space is pre-allocated to avoid resizing during the merge.

- Finding duplicates:

Optimization: Utilize hash tables or sorting to optimize duplicate detection algorithms.

## Can you explain the concept of memory management in the context of array allocation and deallocation?

Memory management in the context of array allocation and deallocation refers to the efficient handling of computer memory to store and release arrays. It involves allocating contiguous memory space for arrays during creation and deallocating it when no longer needed.

Proper memory management prevents memory leaks and enhances program performance by optimizing resource utilization. Memory allocation ensures that arrays have sufficient space to accommodate elements, while deallocation releases memory when arrays go out of scope or are explicitly freed. Efficient memory management is crucial for preventing fragmentation and optimizing the use of available memory resources.

## How do you handle array resizing in dynamic arrays, and what are the performance implications?

Employ strategies like doubling the array size when it reaches full capacity, to handle array resizing in dynamic arrays. This ensures efficiency in insertions. However, this approach leads to occasional over-allocation. Resizing operations have a time complexity of O(n), but the amortized time complexity remains O(1) due to infrequent resizing. This balance optimizes performance, ensuring efficient memory utilization in dynamic arrays.

## What are the differences between array lists and linked lists, and when would you choose one over the other?

Array lists and linked lists differ in their underlying data structures and performance characteristics.

Array lists use a dynamic array, providing constant-time access but can involve resizing, impacting performance. Linked lists use nodes with pointers, allowing for efficient insertions and deletions but with slower random access.

Choose array lists for frequent access and minimal insertions/deletions. Opt for linked lists when dynamic size and efficient insertions/deletions are crucial.

## How do you efficiently search for an element in a sorted and an unsorted array?

Efficiently search in a sorted array using binary search, halving the search space at each step until the target is found or the array is exhausted. Use linear search for an unsorted array, iterating through elements one by one until the target is located, or the entire array is traversed.

## Can you discuss the implementation and advantages of multi-dimensional arrays in high-performance computing?

Multi-dimensional arrays are structures that store data in more than one dimension, organized in rows and columns.

Syntax Example in C++:

Multi-dimensional arrays are stored in contiguous memory locations, facilitating efficient access.

Iterating through multi-dimensional arrays involves nested loops, one for each dimension.

Multi-dimensional arrays are widely used in image processing, simulations, and scientific computations due to their structured representation.

The advantages of multi-dimensional arrays in High-Performance Computing are discussed below.

- Parallel Processing: Multi-dimensional arrays enable parallel processing as computations are distributed across different dimensions.
- Cache Utilization: Contiguous memory storage enhances cache locality, reducing data retrieval times and boosting performance.
- Optimized Libraries: High-performance libraries like BLAS and LAPACK are optimized for multi-dimensional arrays, enhancing computational efficiency.
- Vectorization: Supports SIMD (Single Instruction, Multiple Data) operations, enabling processors to perform multiple operations simultaneously.
- Simplified Code: Expressing complex mathematical operations becomes more concise and readable, contributing to better maintainability.
- Algorithmic Efficiency: Algorithms designed with multi-dimensional arrays exhibit better time complexity, crucial for high-performance computing.
- Data Locality: Facilitates efficient data movement within the memory hierarchy, minimizing delays caused by data access times.

## How do you implement and use jagged arrays (arrays of arrays with different lengths) in programming?

Create an array of arrays to implement and use jagged arrays in programming, where each inner array can have different lengths. Jagged arrays allow flexibility in size unlike a multidimensional array.

Jagged arrays are useful when dealing with uneven data structures or when the size of each dimension is not fixed. They provide dynamic allocation for arrays within an array.

## What is the role of arrays in hash table implementation, and how do they affect collision resolution?

The role of arrays in hash table implementation is to serve as the underlying data structure for storing key-value pairs. Arrays provide direct access to elements based on their indices, making them ideal for quick retrieval.

Arrays in the context of collision resolution, enable the use of separate chaining or open addressing techniques. Each array index in separate chaining, holds a linked list of collided elements. Conversely, open addressing involves placing collided elements in the next available array slot.

## How do you perform matrix operations using arrays, and what are the computational considerations?

Utilize built-in functions in programming languages like NumPy in Python for efficient computation to perform matrix operations using arrays. Matrix multiplication, addition, and subtraction are accomplished through these functions. Computational considerations include time complexity, where larger matrices result in increased processing time, and space complexity, with memory usage proportional to matrix size. Optimize code by leveraging parallel processing and avoiding unnecessary nested loops for improved performance.

## Can you explain the concept of array destructuring in modern programming languages?

Array destructuring in modern programming languages refers to the process of unpacking values from arrays into distinct variables simultaneously. This technique enhances code readability and conciseness by assigning array elements to variables in a single line. It streamlines assignments, making code more expressive and efficient. Popular languages like JavaScript, Python, and Ruby support array destructuring, offering a concise syntax for working with arrays and facilitating cleaner code structures. This practice simplifies the handling of array data, promoting better code organization and reducing redundancy in assignments.

## How do you efficiently merge two sorted arrays?

Employ the merge operation from the merge sort algorithm to efficiently merge two sorted arrays.Β

- Initialize three pointers, two for each array, and one for the merged result.Β
- Compare elements at the pointers and insert the smaller one into the merged array.Β
- Increment the pointer of the array from which the element was selected. Continue this process until both arrays are exhausted.Β
- If any elements remain in either array, append them to the merged array.

The time complexity is O(m + n), where m and n are the sizes of the two arrays.

## What are the challenges and solutions in handling large arrays that exceed memory capacity?

Handling large arrays that exceed memory capacity poses significant challenges in terms of resource management and performance optimization.

Challenge: One major challenge is the potential for memory overflow, where the array size surpasses the available system memory. This leads to crashes or degraded performance.

Solution: Implementing techniques like memory-mapping files or virtual memory to efficiently use storage resources, allowing data to be accessed without loading the entire array into memory.

Challenge: Another challenge is the increased computational cost associated with processing large arrays. This leads to slower execution times and decreased system responsiveness.

Solution: Parallel processing and distributed computing strategies are employed, distributing the workload across multiple processors or systems for improved efficiency.

Challenge: Cache locality issues arise when working with large arrays, impacting the speed of data retrieval.Β

Solution: Optimizing algorithms for spatial and temporal locality enhance cache performance.

## How do you implement a circular buffer using arrays, and what are its applications?

Allocate a fixed-size array and maintain two pointers: one for the head and another for the tail to implement a circular buffer using arrays. Tail wraps around to the beginning when it reaches the end of the array, creating a circular structure.

This Python code demonstrates the basic functionalities of a circular buffer, including enqueue and dequeue operations, along with checks for empty and full conditions.

Applications of circular buffers include efficient data storage in streaming scenarios, such as audio processing and real-time systems. They provide constant-time access and facilitate continuous data flow without the need for shifting elements. Circular buffers are commonly employed in embedded systems, communication protocols, and buffering mechanisms.

## Can you discuss different strategies for handling multidimensional array traversal?

The different strategies for handling multidimensional array traversal are discussed below.

- Linear Traversal: One straightforward strategy for handling multidimensional array traversal is linear traversal, where elements are accessed row by row or column by column in a linear fashion.
- Nested Loops: Implementing nested loops is a common technique, where the outer loop iterates over rows, and the inner loop iterates over columns, facilitating a systematic exploration of each element.
- Row-wise vs. Column-wise: Choosing between row-wise and column-wise traversal depending on the nature of the problem, optimizes performance by leveraging cache locality.
- Zigzag Traversal: An alternate approach involves zigzag traversal, where the direction of movement alternates between rows, ensuring a different exploration pattern.
- Diagonal Traversal: Diagonal traversal is applied for specific scenarios, accessing elements along diagonals, either left to right or right to left.
- Spiral Order: Traversing a matrix in a spiral order, moving from outer layers towards the center, is another effective strategy for multidimensional arrays.
- Block-wise Traversal: Dividing the array into blocks and processing each block separately enhances parallelism and facilitates efficient traversal.
- Recursive Approach: Utilizing recursion when dealing with irregular or nested structures, allows for a flexible traversal mechanism.
- Strided Access: Employing strided access patterns, where elements are accessed with a fixed step size, is beneficial in scenarios requiring selective element retrieval.
- Parallel Processing: Leveraging parallel processing techniques, such as using SIMD (Single Instruction, Multiple Data) instructions, significantly accelerates multidimensional array traversal.

## How do array pointers work in low-level languages like C or C++?

Array pointers in low-level languages like C or C++, serve as memory addresses pointing to the initial element of an array. These pointers facilitate efficient access to array elements by indicating the location in memory. They increment or decrement based on the data type, allowing seamless traversal through the array. Dereferencing these pointers provides direct access to the values stored in the array. Manipulating array pointers is fundamental for efficient memory management and array operations in low-level programming languages.

## What are the implications of array immutability in functional programming languages?

Array immutability in functional programming languages, ensures that once an array is created, its elements cannot be modified. This property has profound implications for program behavior.

- Immutable arrays promote referential transparency, enhancing code predictability by eliminating side effects during array operations.
- Concurrency benefits arise as immutable arrays facilitate parallel processing without concerns of shared mutable state, reducing the risk of race conditions.
- Debugging becomes simpler with immutable arrays, as their unchanging nature makes it easier to trace the source of issues without worrying about hidden modifications.
- Functional languages leverage immutable arrays for efficient memory usage, as sharing unchanged portions between data structures reduces redundancy and optimizes performance.

Overall, array immutability in functional programming fosters code reliability, concurrency advantages, simplified debugging, and optimized memory utilization.

## How do you approach the problem of finding the longest or shortest sequence within an array?

Follow the below steps to find the longest sequence within an array.

- Employ a straightforward linear approach using iteration and tracking variables.Β
- Initialize a counter variable and iterate through the array.Β
- Keep track of the current sequence length and update the maximum length encountered so far.Β
- Reset the counter when the sequence breaks.Β

Here's a basic example code in Python:

Follow the below steps to find the shortest sequence within an array.

- Iterate through the array, keeping track of the current sequence length.Β
- Update the minimum length encountered so far. Reset the counter when the sequence breaks.Β

Here's a concise example in Python:

These algorithms have a time complexity of O(n) where n is the length of the array.

## Can you explain the use of arrays in recursive algorithms and its impact on memory usage?

Arrays in recursive algorithms serve as dynamic data structures, enabling efficient manipulation of elements during function calls. The recursive nature of these algorithms allows for iterative processes without the need for explicit loops.

Arrays in recursive algorithms in terms of memory usage, impact the call stack. Each recursive call adds a new layer to the stack, consuming additional memory. As a result, excessive recursion leads to stack overflow errors.

It's crucial to manage memory effectively when employing arrays recursively, considering the potential for stack growth. Careful implementation and termination conditions are essential to prevent memory-related issues and ensure optimal algorithm performance.

## How do parallel and distributed computing techniques apply to array processing for large datasets?

Parallel computing techniques enable simultaneous execution of operations across multiple elements, significantly enhancing processing speed. This approach involves dividing the dataset into smaller chunks and processing them concurrently.

Distributed computing further amplifies efficiency by distributing the workload across multiple nodes or machines. Each node processes a subset of the array, contributing to a collective computation effort. This strategy minimizes processing time for extensive datasets by leveraging the combined power of multiple computational resources.

Parallel and distributed computing techniques, therefore, play a crucial role in optimizing array processing for large datasets, facilitating faster and more scalable operations.

## Array Coding Interview Questions

Array-related problems are common in coding interviews, testing a candidate's ability to manipulate data structures efficiently. We'll explore a curated list of questions in this section to sharpen your array-handling skills from basic array operations to intricate problem-solving scenarios.

The Array coding interview questions will cover a spectrum of difficulty levels. Each question is designed to assess your understanding of array fundamentals, algorithmic efficiency, and creative problem-solving. Practice these questions to gain confidence and enhance your performance in array-centric coding interviews.

## How would you write a function to rotate an array to the right by a given number of steps?

Use array slicing to rotate an array to the right by a given number of steps in Python. Here's a simple python function to achieve this:

The function rotate_array_right takes an array arr and the number of steps as parameters. We use the modulo operator (%) to calculate the effective steps to handle cases where the number of steps is greater than the array length.

The rotation is performed using array slicing. arr[-steps:] represents the last steps elements of the array, and arr[:-steps] represents the array excluding the last steps elements.

These two sliced arrays are concatenated to form the rotated array, which is then returned.

## Can you code a solution to find the 'Kth' largest element in an unsorted array?

Use the QuickSelect algorithm to find the Kth largest element in an unsorted array. This algorithm is an optimized version of the QuickSort algorithm. It partitions the array based on a chosen pivot, narrowing down the search space.

The key idea is to repeatedly partition the array until the pivot is at the Kth position, meaning we have found the Kth largest element. This is achieved by choosing the pivot strategically and partitioning the array accordingly.

Here's a Python implementation using the QuickSelect algorithm:

This implementation efficiently finds the Kth largest element in an unsorted array with a time complexity close to O(n), making it suitable for large datasets.

## How do you implement an algorithm to check if an array contains duplicate elements within k distance from each other?

Utilize a sliding window approach to implement an algorithm to check if an array contains duplicate elements within k distance from each other. Define a set to keep track of elements within the window and iterate through the array.

This code uses a set to store elements within the current window of size k. The set is updated accordingly as the window slides through the array. The function returns True if a duplicate is found within the window; otherwise, it returns False.

## What is the most efficient way to find the intersection of two arrays?

Utilize the HashSet data structure to efficiently find the intersection of two arrays. You can identify common elements in linear time complexity by converting one array into a HashSet and then iterating through the second array.

Here's an example in Python:

This approach ensures O(n) time complexity for the intersection operation, making it a highly efficient solution.

## Can you demonstrate how to flatten a multidimensional array into a single-dimensional array?

Leverage the numpy library in Python to flatten a multidimensional array into a single-dimensional array. Here's an example using Python:

The flatten() method from the numpy library is used in this example to convert the multidimensional array into a single-dimensional array. This approach simplifies the structure, making it easier to work with flat arrays in various applications.

## How would you write a program to shuffle an array ensuring that each element has an equal probability of appearing in any position?

Use the Fisher-Yates shuffle algorithm to shuffle an array with equal probability for each element in any position. Below is a simple implementation in Python:

This code employs the Fisher-Yates shuffle by iteratively swapping elements in the array, ensuring that each element has an equal chance of appearing at any position. The random.randint function is used to generate a random index for swapping, maintaining uniformity in the shuffling process.

## Can you develop a function to segregate even and odd numbers in an array, maintaining their relative order?

Below is a simple function in Python to segregate even and odd numbers in an array while preserving their relative order:

This function iterates through the array, segregating even and odd numbers into two separate lists. Finally, it concatenates these lists to maintain the original relative order of numbers in the array.

## How do you implement a solution to find all pairs in an array that sum up to a specific number?

Use a hash set data structure to find all pairs in an array that sum up to a specific number.

This code iterates through the array, calculating the complement for each element with respect to the target sum. A valid pair is identified and added to the result if the complement is found in the set of seen numbers. The set is updated as the iteration progresses to efficiently track seen numbers.

## What is the most efficient way to find the smallest and second smallest elements in an array?

Use a single traversal approach to efficiently find the smallest and second smallest elements in an array. Initialize two variables to store the smallest and second smallest elements. Iterate through the array, updating these variables accordingly.

We iterated through the array once, updating the smallest and second smallest elements based on the encountered values. This approach ensures efficiency with a time complexity of O(n), where n is the size of the array.

## Can you code an efficient method for computing the running sum of a 1D array?

Below is an efficient method for computing the running sum of a 1D array:

Explanation:

- We initialize the running sum array with the first element of the input array.
- We iterate through the input array starting from the second element.
- For each element, we add it to the running sum, which is the last element in the running sum array.
- The final result is an array containing the running sum of the input 1D array.

## How to Prepare for an Array Interview?

Follow the key strategies discussed below to prepare for an Array interview.

- Understand Array Fundamentals: Ensure a solid grasp of basic concepts like indexing, element access, and array manipulation.
- Practice Coding Problems: Regularly solve array-related coding challenges on platforms like LeetCode and HackerRank to enhance problem-solving skills.
- Learn Time and Space Complexity: Comprehend the time and space complexity of array operations; optimize solutions to minimize both when solving problems.
- Explore Common Array Patterns: Familiarize yourself with common array patterns such as two-pointer technique, sliding window, and prefix sum for efficient problem-solving.
- Revise Sorting and Searching: Brush up on sorting and searching algorithms, as they are frequently applied in array-related problems.
- Focus on Edge Cases: Pay special attention to edge cases and boundary conditions to ensure robust solutions.
- Master Array-related Data Structures: Understand how arrays interact with other data structures like hash tables and linked lists.
- Review Previous Interviews: Analyze past array-related interview experiences, identify areas for improvement, and refine your approach accordingly.
- Stay Updated with Language-Specific Features: Keep abreast of language-specific array functions and features that can simplify problem-solving.
- Mock Interviews: Engage in mock interviews to simulate real interview conditions and enhance confidence in solving array problems under time constraints.

## Ideal structure for a 60βmin interview with a software engineer

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## List of 50+ Array Coding Interview Problems

In this article, we have listed important Problems on Array which you must practice for Coding Interviews and listed introductory and background topics on Array as well. You must bookmark this page and practice all problems listed.

Table of Contents:

## Basics of Array (+ Implementation)

Types of array.

- Coding Problems on Array ( Important )

Array is a simple Data Structure. Go through the basics of Array and how to implement it in C and C++ Programming Language. This is same for other programming Languages as well. We will attempt Practice Problems following this.

- Row major and Column major order
- Get length of array in C and C++
- Initialize array in C
- Delete an array in C
- 2D array in C ( Important )
- 3D array in C
- Intersection of two arrays
- Time Complexity Analysis of Array

Many do not realize that there are different types of Array. Each type is useful for different problems. Some array techniques like Prefix Sum Array will help you solve key Algorithmic Problems.

Go through the different types of Array:

- Dynamic Array ( Important )
- Time Complexity of Dynamic Array
- Hashed Array Tree: Efficient representation of Array ( Important )
- Suffix Array
- Prefix Sum Array ( Important )
- Bit Array ( Important )
- Bit Mask/ Map
- Array vs Linked List

## Core Techniques of Array

Array is a simple Data Structure but Algorithmic Problems based on array can be challenging to solve efficiently if you are not in practice. Practice these Coding Problems based on Array:

- Least frequent element in an array
- Largest element in array
- Smallest element in array
- K-th largest element in array stream
- Two Pointer technique in Array
- Techniques to find Peak element in array

## Array Rotation

Array Rotation (3 techniques) ( Important )

- Block swap algorithm for array rotation
- Reversal algorithm for array rotation
- Juggling algorithm for array rotation

Partition an Array (2 techniques) ( Important )

- Hoare Partition
- Lomuto Partition
- Move even number to front of array

## Majority Element

- General techniques
- Boyer Moore majority vote algorithm : A very important algorithm for Competitive Programming.

## Practice Problems on Array

- Shuffle an array ( Important )
- Finding 2 elements with difference k in a sorted array ( Important )
- Finding LCM of an array of numbers
- Find GCD of all elements in an array
- Find index such that sum of left sub-array = right sub-array (Equilibrium Index) ( Important )
- Multiple array range increments in linear time O(N)
- String Matching using Bitset ( Important )
- Pass array in function in C in two ways
- Implement 1 Stack in an array
- Implementing two stacks in one array ( Important )
- Implementing K stacks in one array
- Move negative elements to front of array
- Queue using array
- Converting a Sorted Array to Binary Tree
- Minimum Increment and Decrement operations to make array elements equal
- Minimum number of increment (by 1) operations to make elements of an array unique
- Kadane's Algorithm for largest subarray sum
- Minimum number of operations to make GCD of an array K ( Important )
- Minimum number of increment or decrement (by 1) operations to make array in increasing order
- Minimum number of increment (by 1) operations to make array in increasing order
- Smallest Missing Positive Integer
- Set Matrix elements to Zeros
- Make N numbers equal by incrementing N-1 numbers
- Rolling Hash ( Important )
- Maximize the sum of array_i * i
- Find Minimum sum of product of two arrays
- The smallest subset with sum greater than sum of all other elements
- Find the Largest lexicographic array with at most K consecutive swaps
- String Matching using Bitset
- Minimum Product Subset of an array ( Important )
- Maximum Product Subset of an array
- Minimum operations to make GCD of array a multiple of k
- Maximize sum of consecutive differences in a circular array
- 3 Sum Problem
- Closest 3 Sum problem
- Array Interview Questions MCQs

With this article at OpenGenus, you must a strong idea and practice of Array based Problems. Best of Luck with your Interviews and Research.

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π΅οΈββοΈ Elevate your problem-solving skills and gain a deeper understanding of Arrays and Array Manipulation. Prepare to conquer your next interview with confidence and finesse by practicing our DSA Problems.

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Practice array problems for interviews with three levels of difficulty and solutions. Learn how to sort, rotate, find peaks, subarrays, sums, products, and more with arrays.

Learn how to solve array problems with easy, medium and hard examples. Find links to text and video guides, code examples and cheat sheets for common array interview questions.

The key to solving array-based questions is having a good knowledge of array data structure as well as basic programming constructors such as loop, recursion, and fundamental operators.

Practice questions on Arrays. In this article, we will discuss some important concepts related to arrays and problems based on that. Before understanding this, you should have basic idea about Arrays. Type 1. Based on array declaration -. A single dimensional array can be declared as int a [10] or int a [] = {1, 2, 3, 4}.

These problems will help you sharpen your problem-solving skills and prepare effectively for interviews. By tackling these challenges, you can enhance your understanding of Arrays and boost your confidence in interviews. Let's begin and master Arrays by solving Top Array Problems. Top Array Interview Questions and Answers: Question 1.

Our platform offers a range of essential problems for practice, as well as the latest questions being asked by top-tier companies. Explore; Problems; Contest; Discuss; Interview. Store Study Plan. See all. Array 1678. String 705. Hash Table 613. Dynamic Programming 510. Math 502. Sorting 406. Greedy 365. Depth-First Search 288. Database 272 ...

Subscribe to see which companies asked this question. You have solved 0 / 1681 problems. Show problem tags # Title Acceptance Difficulty Frequency; 1: Two Sum. 53.0%: Easy: 4: Median of Two Sorted Arrays ... Medium: 33: Search in Rotated Sorted Array. 41.1%: Medium: 34: Find First and Last Position of Element in Sorted Array. 44.7%: Medium: 35 ...

Top 50 Array Problems with Solutions A handpicked selection of array-based coding problems for interview preparation, with documented solutions in popular languages. Boost your problem-solving skills and contribute to this open-source project. Happy coding! - pranaydas1/Top-50-Array-Problems

Solve 21 standard Arrays problems to learn and practice data structures and algorithms. These problems range from beginner to intermediate level and cover topics such as searching, sorting, merging, and compressing arrays.

Test your problem solving skills with arrays on HackerRank, a platform for coding interviews. Choose from easy, medium or hard levels and solve challenges with different array operations.

Learn the basics, advantages, disadvantages, and common terms of arrays, and how to solve problems involving arrays with various techniques. Find essential and recommended questions, time complexity, and learning resources for array topics.

When I first started, even solving easy problems was a challenge. If you're in the same boat, I highly recommend starting with Level 1 of the GeeksforGeeks 50 Array questions, assuming you haven ...

Arrays Prepare for you upcoming programming interview with HackerRank's Ultimate Interview Preparation Kit

9. Intersection of Two Arrays II. Problem: Given two arrays, write a function to compute their intersection. Solution: Use two dictionaries (hash tables) to store the frequency of elements in both ...

on arrays. Nothing for you, folks! Try some other topics. Solve from more than 2000 coding problems and interview questions on 100+ different topics. HackerEarth is a global hub of 5M+ developers. HackerEarth is a global hub of 5M+ developers. We help companies accurately assess, interview, and hire top developers for a myriad of roles.

Top 50 Algorithms and Coding Interview Questions. Without any further ado, here is my list of some of the most frequently asked coding interview questions from programming job interviews: 1. Array Coding Interview Questions. An array is the most fundamental data structure, which stores elements at a contiguous memory location.

Follow the key strategies discussed below to prepare for an Array interview. Understand Array Fundamentals: Ensure a solid grasp of basic concepts like indexing, element access, and array manipulation. Practice Coding Problems: Regularly solve array-related coding challenges on platforms like LeetCode and HackerRank to enhance problem-solving ...

The first line of input contains two integers n and k. The second line contains n integers β the elements of the array. The absolute values of elements do not exceed 104. The problem consists of two subproblems. The subproblems have different constraints on the input. You will get some score for the correct submission of the subproblem.

This is same for other programming Languages as well. We will attempt Practice Problems following this. Array. Row major and Column major order. Get length of array in C and C++. Initialize array in C. Delete an array in C. 2D array in C ( Important) 3D array in C.

Uncover the secrets of solving well-known array problems that are frequently asked in interviews. We'll not only walk you through the problems but also provide strategic insights on how to approach and master them. π΅οΈββοΈ Elevate your problem-solving skills and gain a deeper understanding of Arrays and Array Manipulation. Prepare to ...

That's all in this list of array interview questions for programmers. If you have solved all this problems then you definitely have good preparation. You can now tackle any array based coding problems, even though you will see it first time, mostly on coding interviews from top software product companies like Amazon, Google, Microsoft and Facebook.

Solve the most popular arrays interview questions. Prepare for DSA interview rounds at the top companies. ... Math & Bit Manipulation. 15. Dynamic Programming. 27. Greedy Algorithm. 4. Graphs. 26. String & Tries. 15. About. FAQs. ... Topic-wise Problems. Dynamic Programming Interview Questions; Linked List Interview Questions;

Welcome to the daily solving of our PROBLEM OF THE DAY with Saurabh Bansal. We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Arrays but also build up problem-solving skills. Given an array, arr of integers, and another number target, find three integers in the array such that their sum is ...