Data structures Interview Questions!!! Are you prepared to tackle data structures? Head on and land your dream job? If you’re preparing for a coding interview, comprehension of data structures is essential for success!

Nonetheless, don’t be concerned: with the right approach and preparation, you can easily handle even the most difficult interview questions about data structures!

From beginners just starting started with coding to experienced experts looking for their next employment, this post includes crucial instructions for confidently answering data structure interview questions.

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Data structures Interview Questions and Answers:

1. What are data structures in computer science?

Data structures are methods of organizing and storing data in a computer so that it can be accessed and manipulated efficiently.

2. What are the basic data structures?

Basic data structures like the array and array list, also known as the list and Python, are commonly used by companies.

3. Why is it important to understand advanced data structures?

Understanding and comprehending advanced data structures is crucial for a successful career in computer science as they are used in various applications such as the Undo-Redo button in Google Docs, Google Maps on phones, and auto-complete features in text messages.

4. What is big O (Order Of) notation?

Big O notation is the industry standard for storing large amounts of data. It is used to score a data structure based on four criteria: accessing a specific element, searching for a specific element, inserting an element, and removing an element.

This helps create a report card of how efficient a data structure is, providing an overview of its strengths and weaknesses.

5. What are some examples of advanced data structures?

Examples of advanced data structures include stacks, queues, linked lists, dictionaries, trees, tries, heaps, and graphs.

6. What are the benefits of using data structures in programming?

Data structures provide an organized way to store and access data, which can improve the efficiency and performance of a program. They also allow for easier modification and manipulation of data.

7. What is the purpose of data structures in computer science?

The purpose of data structures in computer science is to provide an organized way to store and access data, as well as to manage and manipulate information within a program.

8. What are some common types of data structures?

Some common types of data structures include arrays, arrays lists, stacks, queues, linked lists, dictionaries, trees, tries, heaps, and graphs.

9. What is the importance of understanding the efficiency of data structures in programming?

Understanding the efficiency of data structures is crucial for programming and gaining a competitive edge in the field. By examining the report card of a data structure, one can make informed decisions about which structure to use for specific tasks.

10. How is the efficiency of data structures measured?

Big O notation is used to grade data structures based on their efficiency in accessing, searching, inserting, and deleting elements.

These functions are scored using big O notation time complexity equations, which work by inserting the size of the data set as an integer n and returning the number of operations needed by the computer before the function can finish.

11. What is the syntax for big O notation time complexity equations?

The syntax for these equations includes a big O and a set of parentheses, which contain a function that correctly returns the number of operations needed to be run by the computer.

12. What are the four metrics used to measure the efficiency of a data structure?

These metrics are accessing a specific element, searching for a specific element, inserting an element, and removing an element.

13. What is the gold standard time complexity equation in big O notation?

The most efficient time complexity equation is O of 1, which means tasks can be completed in a

14. What is the fastest type of time complexity equation in big O notation?

O log n is the fastest type of time complexity equation, providing fast completion even with a smaller data set.

15. What are the two inefficient time complexity equations in big O notation?

O and squared and O to the n are both inefficient equations, as they are exponential in structure and become more inefficient as the data set size increases.

16. What is the first type of equation that is relatively bad in terms of efficiency?

O and log n is the first type of equation that is relatively bad in terms of efficiency. The graph of volume versus operations shows a linear but increasing graph, meaning it won’t be better in terms of efficiency as the size of the data set increases.

17. What are some features to consider when deciding on which data structure to use in a program?

While big O notation is an important factor to consider, many data structures have specific features that separate them from the rest and provide additional functionality.

18. What is the concept of time complexity equations and their importance in grading data structures?

Time complexity equations are used to measure the efficiency of a data structure based on its performance in accessing, searching, inserting, and deleting elements within itself.

These equations work by inserting the size of the data set as an integer n and returning the number of operations needed by the computer before the function can finish.

The worst-case scenario is used to determine which data structures can perform under the worst conditions.

19. What are the three attributes of an array?

The three attributes of an array are a name, a type, and a size.

20. What is a type in an array?

The type of an array is crucial, as it must hold all the same type of information. An array cannot contain both integers and strings, so it must be either an array of only integers or only strings.

21. What is a size in an array?

The size of an array is a set integer that is fixed upon creation and represents the total amount of elements that can be stored within the array.

22. What is the purpose of an array in programming?

An array is a list of similar values grouped together in a central location, which can be used to store any list of values of the same type, such as integers, strings, floats, and objects.

23. What are the two ways to instantiate an array in most languages?

There are two ways to instantiate an array in most languages: populating the array with desired elements, or setting a specific size for the array and slowly populating it later on.

24. How is an array created in Java and C-sharp?

In Java and C-sharp, there are slight differences in the syntax for creating an array, but it generally follows a set pattern. The type of the array followed by a name for the array is set equal to the size for the array, which cannot be changed outside of the initial call.

25. How is information stored within an array?

The simplest way to get information stored within the array is through a numerical index.

26. What is an index in an array?

An index is an integer that corresponds to an element within an array, starting at zero.

27. How is information stored within an array?

To retrieve information from a certain position within an array, reference it using both the array’s name and the index number of the element you wish to retrieve.

28. How is information stored within a two-dimensional array?

A two-dimensional array is a type of array with an array at each element, similar to any other array except for having a different array with its own size and indexes.

To reference an element within a two-dimensional array, you need to use two indexes: the first number is the index of the column you want to reference, and the second number is the index of the row you want to reference.

29. What are the four criteria for evaluating the efficiency of arrays?

The four criteria for evaluating the efficiency of arrays are accessing elements within an array, searching for an element within an array, inserting an element in an array, and deleting an element from an array.

30. What is the worst-case scenario for searching through an array?

In the worst-case scenario, searching through an array to find a particular element is O of N.

31. What is the advantage of arrays over other data structures?

Arrays offer a significant advantage over other data structures in terms of instantaneous accessing power and searchability.

32. What are the types of arrays in terms of dimensionality?

There are other types of arrays, such as three-dimensional, four-dimensional, and more, for containing large amounts of advanced relational data.

33. How is information stored in a three-dimensional array?

A three-dimensional array is a type of array with two arrays in each element, similar to any other array except for having two different arrays with their own size and indexes.

34. What is an ArrayList and what is its relationship with arrays?

An ArrayList is an advanced data structure that is a growing array that can be expanded as the programmer needs it. It is backed by an array in memory, making it a scaffolding system for code.

ArrayLists provide many of the functionality of arrays and some from array lists. However, they are not separate entities in the base version of Python, as they are signed together into a single data structure called lists.

35. How do you initialize an ArrayList?

To initialize an ArrayList, you must invoke the ArrayList class when defining it, which is done at the beginning of both initializations. You can either enter an integer to define a size for the ArrayList or leave a blank.

36. What is the difference between an ArrayList and an array list?

An ArrayList is an evolved form of an array, offering more functionality and power than an array. It belongs to the prebuilt ArrayList class, which provides prebuilt functions for accessing, changing, adding to, or deleting from it.

This makes it especially useful for data structures that need to be programmed by hand. On the other hand, an array list is a list-like data structure that can grow and shrink dynamically, providing more functionality than an ArrayList.

37. What are the common methods available in ArrayLists?

In this series, six common methods are covered: add method, remove method, get and set methods, clear method, and two array method. These methods are pre-programmed and can be called on a pre-made ArrayList.

38. What is autoboxing and how is it used in ArrayLists?

Autoboxing is used to convert primitive integers into integer objects with a value of two, making it easier to add them.

The add method takes in an object and an index value, and the ArrayList automatically shifts the integers two and five to the right one to make space for the integer one.

39. How can you insert a number into an ArrayList?

To insert the number three at the second index, call the add method with the integer three and the index location two.

Be careful and knowledgeable about the order of the methods arguments, as mixing them up could cause the computer to insert the attempted index location at a different location.

40. What are the six major functions provided by the ArrayList class?

The ArrayList class has six main functions: remove, get, set, clear, and two Arraymethod, which converts an ArrayList to an Array.

41. What is the remove method in the ArrayList class?

The remove method in the ArrayList class removes an object if one is present at an index location.

42. What is the two Array method in the ArrayList class?

The two Array method in the ArrayList class converts an ArrayList to an Array, which is useful for combining the strengths and weaknesses of ArrayList.

43. What is the difference between ArrayList and Array?

The ArrayList is a data structure with O of 1 accessing power, meaning it can return the value at an index location in instantaneous time.

An Array, on the other hand, is built into most languages, meaning it doesn’t have any pre-made methods for interaction or modification.

44. What is the time complexity equation for inserting an element in an ArrayList?

The time complexity equation for inserting an element in an ArrayList is O of n.

45. What is the time complexity equation for deleting an element from an ArrayList?

The time complexity equation for deleting an element from an ArrayList is O of n.

46. How can an ArrayList be converted to an Array?

The two Array method in the ArrayList class can convert an ArrayList to an Array, which is useful for combining the strengths and weaknesses of ArrayList. It takes in no arguments and converts the ArrayList into an Array.

47. What is the difference between random access and sequential access data structures?

Random access data structures allow accessing any element within a structure by calling its index location, resulting in instantaneous returns.

This is different from sequential access data structures, which can only be accessed in a particular order with dependent elements.

48. What is a stack?

A stack is a sequential access data structure that follows the LIFO principle, meaning that the first element added to the stack is the first one to retrieve. Accessing or modifying an element within the stack is limited to one point, unlike arrays and array lists.

49. What are the fundamental methods in a stack?

The stack class comes with two fundamental methods, push and pop, which are essential for interaction and modification.

Additionally, peak and contains are useful methods found in the stack class associated with most programming languages.

50. What is the time complexity of using a stack?

The time complexity of using a stack is O(n), as accessing an element requires popping off every element above it. This is one of the major drawbacks of using a stack, as it requires multiple operations equal to the size of the stack.

51. What is the difference between push and pop in a stack?

Push adds an object to the top of the stack, while pop removes it from the top without any arguments.

52. What is the peak method in a stack?

The peak method allows accessing the value at the top of the list without actually removing it, while contains returns a Boolean indicating whether an item is contained within the stack.

53. What is the advantage of using a stack-based programming system?

The data only flows in and out of a single point, making inserting or removing an object immediate. Push adds an object to the top of the stack, while pop removes it from the top.

This makes the stack-based programming system more efficient than arrays and array lists, as it doesn’t require rearrangement or removal of elements.

54. What is the disadvantage of using a stack?

The time complexity of using a stack is O(n), as accessing an element requires popping off every element above it. This is one of the major drawbacks of using a stack, as it requires multiple operations equal to the size of the stack.

55. What are stacks used for?

Stacks are used for tracking active functions or subroutines, and are the backbone for recursion. Examples of stack-based functions include the undo redo button in word processors and backpaging on web engines.

56. What is the difference between stacks and queues?

The main difference between stacks and queues is the order in which elements are removed. Stacks follow the (“last in, first out”) LIFO principle, meaning last in first out, while queues follow the phyfo methodology, or first in first out.

Additionally, elements are added to and removed from different locations in the two data structures.

57. How does the location of adding and removing elements affect the order in which they are removed?

In the stack, elements are added to the top and removed from the top, following the LIFO principle. In contrast, elements in a queue are added to the back and removed from the front, following the phyfo methodology. This ensures that the first element added to the queue is the first one to be removed.

58. Why are stacks and queues dynamic data structures?

Both stacks and queues are dynamic data structures because they can change size as elements are added and removed.

59. What is the difference between on queue, D Q, peak, and contains?

On queue adds elements to the tail of the queue, D Q removes elements from the head, peak delivers the queue’s front object without arguments, and contains returns a Boolean value indicating whether the queue contains that object.

The entire queue of size n must be iterated to access an element, making it an O event. Searching requires iterating across the queue to find an element, making it an O event.

60. How can developers optimize their queue usage?

Developers can optimize their queue usage by understanding the time complexity equations of accessing an element, inserting, and deleting from a queue.

By knowing these methods, they can make informed decisions about how to use queues in their code and improve overall performance.

61. What are queues used for?

Queues are used for job scheduling, printer tracking, and zero shutter lag in Google’s Pixel phones. They follow the FIFO principle, which means first in, first out.

62. How do queues differ from stacks?

Queues and stacks differ slightly in the methodology used to add and remove objects. In a queue, elements are added to the back and removed from the front, following the FIFO principle.

In contrast, stacks follow the life principle, meaning last in first out, and elements are added to and removed from the top.

63. What is a linked list?

A linked list is a sequential access linear data structure in which every element is a separate object called a node. Each node has two parts: the data and the reference, or pointer, which points to the next node in the list.

64. What are the head and tail nodes of a linked list?

The head node of a linked list is an arbitrary label representing a node containing some form of data and a reference to the next node in the linked list.

The last node in a linked list, also known as the tail node, points towards a null value as it tells the computer that we reached the end of the linked list.

65. How are nodes added and removed from a linked list?

To add and remove nodes from a linked list, three different methods are used: adding to and removing a node from the head, middle, and tail of a linked list.

Each node has a value representing the data inside the node and a pointer pointing it to the next node.

66. How do you add a new node to the head of a linked list?

To add a new node to the head of a linked list, you make its pointer point to the current head node.

67. How do you remove a node from the head of a linked list?

Removing a node from the head of a linked list is as simple as setting its pointer to null, which removes it from the flow of information.

68. What is the difference between adding and removing a node from the middle of a linked list?

Adding and removing a node from the middle of a linked list involves finding the previous and next nodes in the list and manipulating their pointers to insert or remove the new node.

69. What is the difference between adding and removing a node from the tail of a linked list?

Adding and removing a node from the tail of a linked list involves setting the pointer of the last node to point to null or finding the previous node and updating its pointer to point to the new node.

70. Why are linked lists useful for programming?

Linked lists are useful for programming because they allow for easy insert and removal of elements at any location, unlike stacks or queues.

They are essential for various fields such as job scheduling, printer tracking, and zero shutter lag in Google’s Pixel phones.

71. How do you insert a node in the middle of a linked list?

To insert a node in the middle of a linked list, you make the pointer of the new node point to the node after the desired location and set the node before the location to point towards the new node.

72. How do you remove a node from the middle of a linked list?

Removing a node from the middle of a linked list involves making the pointer of the previous node point to the node after the one being removed and setting the pointer of the node to null.

73. How do you insert a node at the end of a linked list?

To insert a node at the end of a linked list, you modify the tail node of the linked list, which currently points towards a null value. To add a node, make the current tail’s pointer point towards the node you want to add.

74. How do you remove a node from the end of a linked list?

To remove the tail from a linked list, set the previous tail to point towards a null value instead, leaving the current tail node disconnected from the linked list.

75. What is the time complexity of accessing an element in a linked list?

Accessing an element in a linked list is O of n, as it requires starting at the beginning and cycling through every node within it before accessing the desired element.

76. What is a doubly linked list?                                         

A doubly linked list is a sequential access data structure that stores data in the form of nodes. It allows for traversing both forwards and backwards to the previous node in the list using pointers.

77. What is the difference between a singly linked list and a doubly linked list?

In a singly linked list, each element has a pointer that points to the memory location of the next node in the list. In a doubly linked list, each element has two pointers: a next pointer and a previous pointer.

The next pointer points to the next object in the list, either another node or null value, while the previous pointer points to the previous node in the list.

78. How do you create a doubly linked list?

To create a doubly linked list, you start with a head node, which has its previous and next pointers pointed towards a null value. When adding another node, the new node’s next pointer points towards the new node instead of null, and the new node’s previous pointer points to the head node.

The new node’s next pointer points to the next node, and the new node’s previous pointer points to the previous node.

79. What are the three methods for adding nodes to a doubly linked list?

The three methods for adding nodes to a doubly linked list are adding to the head, middle, and tail.

80. How do you add a node to the head of a doubly linked list?

To add a node to the head of a doubly linked list, you set its previous pointer to null, set the new node’s previous pointer to null, and finally set the atom node’s previous pointer to point towards the A node.

81. How do you add a node to the middle of a doubly linked list?

To insert a node into the middle of a doubly linked list, set the node to point toward the previous node to the desired position, the node to point toward the node after the desired position, and the node’s next and previous pointers to the new node.

82. How do you add a node to the tail of a doubly linked list?

To add a node to the tail of a doubly linked list, set the next pointer of the current tail to point towards the new node you want to become the tail, set the previous of the new node that you’re adding to point towards the current tail of the list, and finally make the new node’s next pointer to point towards a null value.

83. What are the time complexity equations for adding nodes to a doubly linked list?

The time complexity equations for adding nodes to a doubly linked list are O-event for all four cases, and sometimes O-one for both inserting and deleting.

84. What is the difference between adding and removing nodes from a singly linked list and a doubly linked list?

Each entry of a single linked list points to the memory location of the next node. Each doubly linked list member contains two pointers: next and prior.

Next points to the next node in the list, either another node or null value, whereas previous points to the previous node.

85. What are some applications of doubly linked lists?

The adaptability and fast operating speed of doubly linked lists make them useful for stack-like functionality, such as browser caches, undo redo functionality, and applications that utilize open recent functionality.

Doubly linked lists are also useful for storing information in a shell, making it easier to move information around.

86. What are the two important restrictions when working with dictionaries?

The two important restrictions when working with dictionaries are that every key must only appear once within the dictionary and each key can only have one value.

87. What are hash functions used for?

Hash functions are used to map all the keys for a given dictionary to specific index locations in an array, allowing them to be retrieved easily.

88. What is a good hashing function?

A good hashing function takes in a value and returns an index location, ensuring that the key is reliably placed in the table for later retrieval.

89. How do dictionaries store keys and key value pairs?

Dictionaries are built upon hash tables, and the keys and key value pairs are stored at indexes determined by hash functions.

90. What is a hash collision?

A hash collision occurs when two different dictionary keys are stored at the same index location in a hash table.

91. How can hash collisions be resolved?

Hash collisions can be resolved through open addressing or closed addressing. Open addressing stores the key in another index location, separate from the one returned by the hash function. Close addressing uses linked lists to chain together keys that result in the same hash value.

92. What are the average time complexity equations for accessing, searching for, inserting, or deleting a key value pair from a dictionary?

Assuming the worst-case scenario, the average time complexity equations for accessing, searching for, inserting, or deleting a key value pair from a dictionary are O of 1.

93. What is the difference between dictionaries and trees?

Dictionaries store information in the form of key value pairs, while trees store data hierarchically, allowing for more abstract connections and multiple branches.

94. What are the time complexity equations for trees?

Time complexity equations for trees are more complex than dictionaries and are not included in the data structures series.

95. What is a tree?

A tree is an abstract data structure consisting of a series of linked nodes connected together to form a hierarchical representation of data.

96. What is a tree in computer science?

A tree in computer science is a type of data structure that consists of vertices, edges, root nodes, parent nodes, and leaf nodes. It is useful for organizing and maintaining complex data in trees.

97. How is a tree structured?

A tree starts with a root node, which is the topmost node. Two vertices connected to the root node are called child nodes, and the root node is a parent node to these two child nodes.

A tree can have many different titles depending on its position and the other nodes connected to it.

98. What are the two properties of a tree?

The height and depth of a tree are two properties. The height is the number of edges on the longest possible path down towards a leaf, while the depth is the number of edges required to get from that particular node to the root node.

99. What is a binary search tree?

A binary search tree is a popular variant of the tree data structure. It has three restrictions: a node can have at most two children, a child to the left has a value less than or equal to itself, and a child to the right has a value greater than or equal to itself.

100. How is a binary search tree used?

The binary search tree is useful for organizing and maintaining complex data in trees, such as finding the minimum and maximum values and searching for a specific value or range of values.

It can also be used as a basis for more complex data structures such as heaps and tries.

In conclusion, data structures are an important tool for organizing and storing data in a computer.

They allow for the efficient storage, retrieval, and manipulation of data and are utilized in a variety of applications, from simple programs to sophisticated systems.

Understanding data structures is an essential component of learning computer science, whether you’re a beginner or an experienced programmer.

Using the appropriate data structure for the job allows you to build more efficient, scalable, and maintainable code.