Commonly Asked Data Structure Interview Questions
Data structure interview questions are a big part of interviews for many data roles. To answer them, you should know the data structures we’ll cover here.
The first step in understanding data algorithms is understanding data structures, which are algorithms’ building blocks.
They are vital in implementing and optimizing algorithm performance. Data structures’ importance is reflected in the knowledge about them being regularly tested in interviews where algorithms are a required skill.
In this article, we will explain different data structures and solve one actual interview question for each.
Understanding Algorithms and Data Structures
An algorithm is a step-by-step sequence of instructions whose purpose is to perform a specific task or solve a problem. It takes these instructions and applies them to inputs to produce outputs.
Some examples of what these tasks and problems might be are data sorting, finding the shortest path in a network, searching for an item in a dataset, data processing, automation of various repetitive tasks (e.g., sending emails or inventory management), optimization (e.g., optimizing search engine results or network performance), or use in artificial intelligence and machine learning (e.g., recommendation systems or voice recognition).
A data structure is a specialized format for organizing and storing data. There are many different data structures, each with its own characteristics and uses, which we’ll discuss in a heap, pun intended.
Why are data structures important for algorithms? Different data structures store, retrieve, and manipulate data differently during the algorithm execution. Because of that, data structures are crucial in optimizing algorithms.
Different Data Structures Explained
In the following sections, I will explain these data structures.
The explanations will be followed by solving the actual data structure interview questions you could encounter at your job interview.
Since we’ll write algorithms in Python, it’d be helpful if you’re familiar with data structures in Python.
Arrays
Definition: Arrays are linear collections of elements. Each element is identified by an index. Arrays are zero-indexed, meaning that the first element in an array is always 0, the second is 1, and so on.
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Typical Use Examples:
Pros: Arrays' two main pros are that they are simple to implement and use and that their indexes allow fast access to elements (O(1) time complexity ).
Cons: For the fixed-size arrays, one significant con is that they can’t be dynamically resized.
Due to that, another con is that insertion and deletion of data can be costly (O(n) time complexity ). The second con is only partially valid for dynamic arrays; it applies only to inserting data in the middle or resizing. However, when appending data at the end, the time complexity is O(1) for dynamic arrays.
One of the popular interview questions involving arrays is to find the largest value in the array, such as in this question by Twitter.
Link to the question: https://platform.stratascratch.com/algorithms/10431-maximum-number-in-array
We can have this array to test the algorithm (Test 1): [3, -2, 7, 3, -9, 12]
The algorithm written in Python is this.
def find_largest_number(arr):
largest = arr[0]
for num in arr:
if num > largest:
largest = num
return largest;
Explore this code editor and review the solution by reading the full blog here ??
Strings
Definition: A string is primarily a data type but can also be seen as a data structure, as it’s implemented in arrays or lists. It’s a sequence of characters, and it stores data as text. Several typical operations are performed on strings, such as concatenation, searching, and slicing.
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Typical Use Examples:
Pros: Strings are immutable in many programming languages, which can be useful when you want to prevent unintended modification.
Strings also come with a rich set of manipulation and comparison operations, such as concatenation, substring extraction, pattern matching, trimming, case conversion, and comparison. These operations make handling and manipulating textual data quite simple.
Cons: If the strings are immutable, some operations, such as concatenation, can be computationally expensive. Concatenation, in this case, doesn’t change the existing string but creates a new one, which involves memory allocation and copying the existing strings.
In some programming languages, strings may have a fixed length. This often results in allocating a maximum possible size for strings in advance, which could mean there’s a lot of wasted memory not used sufficiently.
For more information, read our article about working with strings in Python .
The interview question by EY asks you to perform a common task using an algorithm: sort a string alphabetically.
Link to the question: https://platform.stratascratch.com/algorithms/10407-alphabetize-strings
Here’s the question solution.
def sort_string(string):
return ''.join(sorted(string));
Explore this code editor and review the solution by reading the full blog here ??
Linked Lists
Definition: Linked lists are linear data structures consisting of nodes. Each node contains a data part and a pointer or reference (depending on the programming language) that leads to the next node.
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Typical Use Examples:
Pros: Linked lists are dynamic data structures that easily grow and shrink, which makes them ideal for regularly changing data.
Along with that comes the ease with which data is inserted or deleted.
Cons: Linked list searches are slower than in arrays, for example. To access data at a specific position, the linked list must be traversed from its beginning, following each pointer until the desired data is reached. This indirect access to data through the index has a time complexity of O(n); n is the number of elements in the linked list.
Another linked list con is memory requirement since they contain not only data but a pointer to the next node. Required memory could be significant for large linked lists.
Stacks
Definition: Stacks are linear data structures that follow the LIFO method – Last In, First Out. It relates to the order in which data is added to and removed from a stack. So, in other words, the last added element will be the one to be removed first to access data in the middle or at the bottom.
Three main operations connected with the stacks are:
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Typical Use Examples: Stacks are used for tasks that require reversing, for example:
Pros: Due to their characteristics, stacks are efficient in managing function calls, parsing, and backtracking.
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They are also very adaptable since they’re easy to implement in arrays, linked lists, or dynamic arrays.
Cons: Their pro – the LIFO principle – is also a con when you want random access to elements. This would require removing all the elements from the top to reach if the required element is not on the top of the stack, which is not very efficient.
It limits their use cases, making them unsuitable for scenarios where frequent searches are required.
Queues
Definition: Queues are, too, linear data structures. Unlike stacks, queues follow the First-In-First-Out (FIFO) principle for adding and removing data. This means that the first element added to the queue is the first one to be removed from the queue.
Unlike in stacks, elements in queues are not stacked on top of each other but lined one after the other. Based on this, the operations characteristic for queues are:
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Typical Use Examples: Queues are primarily used in scheduling tasks such as:
Pros: The queues’ characteristics ensure fair and orderly access to the element, where every element waits its turn to be processed.
This makes them ideal for task scheduling and management, as they allow sequential processing.
Cons: Their fair and orderly data processing is also a con because elements not at the front of the queue can’t be accessed directly; all preceding elements must be dequeued first. This could lead to inefficient data processing and is not suitable for random access.
Trees
Definition: A tree is a hierarchical data structure where each node holds a value and can be connected to many children nodes. There are several types of trees, with a binary tree (each node has at most two children) being the most commonly used.
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Typical Use Examples:
Pros: Since trees are inherently hierarchical data structures, they are – as expected – perfect for representing hierarchical data. Due to their consisting of nodes, traversing hierarchical data becomes easy.
A special type of tree structure called a balanced tree , such as an AVL tree or a Red-Black tree , keeps its height in check. This makes it efficient, which is great for processing large amounts of data, as the operations are performed in O(log n) time, where n is the number of nodes.
Cons: Of course, if a tree becomes unbalanced, then it’s a con. In other words, the tree becomes inefficient, where operations could degrade to being performed in O(n) time complexity.
Another con is that trees are not easy to implement, as they require complex algorithms that must handle pointers and maintain tree integrity.
Graphs
Definition: A graph is a data structure consisting of nodes or vertices . This might seem similar to trees and, indeed, trees are a specialized type of graph. To be more specific, a hierarchical and more rigid kind of graph.
Graphs don’t have a root node, the relationships between nodes are arbitrary, and each node possibly has any number of edges, i.e., connections to other nodes.
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Typical Use Examples:
Pros: Graph’s nonlinear nature makes them flexible and suitable for representing complex and multiple relationships between entities, with examples given above.
Graphs also support various traversal algorithms , for example, Depth-First Search (DFS) and Breadth-First Search (BFS), which are used in exploring a graph’s nodes and edges.
Cons: Their complexity makes graphs memory-intensive. For example, representing a graph using adjacency matrices requires O(V2) space, with V being the number of vertices.
In addition, performing operations on graphs requires complex algorithms. Examples of such operations include finding the shortest path in weighted graphs (Dijkstra’s algorithm ), detecting cycles, graph coloring , network flow , and finding the minimum spanning tree .
Hash Tables
Definition: Hash tables are data structures that store key-value pairs and use a hash function to determine the index for storing elements.
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Typical Use Examples:
Pros: Due to their design, which uses a hash function to compute an index from a key, which is then used to access the particular value directly, hash tables support high-speed data lookups. On average, the value can be accessed in constant time, O(1).
Hash tables are also memory-efficient, as they start small when implemented and resize only when the ratio of elements to table size exceeds a certain threshold.
Cons: Hash tables are susceptible to hash collisions, i.e., two or more different values being hashed to the same index. This requires special handling (e.g., chaining and open addressing ), which, in turn, can degrade hash table performance.
In addition, a good hash function can minimize collisions; designing and choosing one can be challenging and time-consuming.
Heaps
Definition: Heaps are tree-based (a complete binary tree ) data structures that satisfy the heap property . That property means that each parent node is
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Typical Use Examples:
Pros: In heaps, the priority element is always at the root (the highest or the lowest priority), which makes them ideal for priority-based tasks; they can quickly access the maximum or minimum element with constant time being O(1).
Cons: The above characteristic also makes heaps not very suitable for searching arbitrary elements. They don’t maintain any specific order among siblings, which makes it necessary to potentially go through each element when searching.
Heaps are also relatively complex to implement and maintain compared to arrays or linked lists. This is because the heap property has to be maintained constantly, which could cause an element to ‘bubble up' through the tree (when adding an element) or ‘bubble down’ (when removing an element).
Conclusion
Data structures are an essential stepping stone for understanding and working with algorithms. In this article, we covered several of the most common data structures:
Their importance is exemplified in data structure interview questions, which are a common feature in technical job interviews.
These interviews revolve around coding, which is also the best way to practice data structure interview questions. In particular, you can achieve that by solving algorithm questions involving diverse data structures.
For more details about data structure interview questions and solutions, read the full article here ??