Top 70+ Most asked CS & AI Engineering Interview Questions for Job Preparation

  by Vishesh Namdev    01-01-2025

For anyone entering the dynamic fields of AI and data science, this section offers questions covering machine learning, deep learning, data visualization, and statistical analysis. Be prepared to discuss neural networks, natural language processing, or optimization algorithms. These questions are ideal for careers in AI research, data engineering, and analytics.

70+ CS & AI Engineering Interview Questions - The Royal Coding

  1. 1. Programming and Data Structures

  2. Question: What are the differences between a stack and a queue?

    Answer: A stack follows LIFO (Last In First Out), while a queue follows FIFO (First In First Out).

  3. Question: Explain the time complexity of searching an element in a binary search tree (BST).

    Answer: The time complexity is O(h), where h is the height of the tree. In a balanced BST, it's O(log n).

  4. Question: What is the difference between a binary search tree (BST) and a binary tree?

    Answer: A binary tree is a tree data structure in which each node has at most two children (i.e., left child and right child). A binary search tree is a binary tree where for each node, the values in the left child are less than the node's value and the values in the right child are greater than the node's valu

  5. Question: What is the time complexity of sorting a list of n elements using the quicksort algorithm?

    Answer: The average time complexity is O(n log n). However, in the worst case, it can be O(n^2).

  6. Question: What is the difference between a hash table and a binary search tree?

    Answer: A hash table uses a hash function to map keys to indices of a backing array, while a binary search tree uses a binary tree data structure to store keys and their corresponding values.

  7. Question: How is memory management handled in C++?

    Answer: Memory management in C++ is manual, using operators like new and delete for dynamic allocation and deallocation.

  8. Question: Describe how dynamic programming works and provide an example problem.

    Answer: Dynamic programming solves problems by breaking them into overlapping subproblems and using memoization. Example: Fibonacci sequence.

  9. Question: What is the difference between depth-first search (DFS) and breadth-first search (BFS)?

    Answer: DFS explores as deep as possible along a branch before backtracking; BFS explores level by level.

  10. Question: How is memory allocated for variables in static and dynamic memory allocation?

    Answer: Static memory allocation occurs at compile time; dynamic memory allocation happens at runtime.

  11. Question: What is tail recursion, and how does it differ from normal recursion?

    Answer: Tail recursion is a recursion where the recursive call is the last operation. It can be optimized by compilers to avoid stack growth.

  12. Question: Describe how a hash table works. How do you handle collisions in hashing?

    Answer: A hash table uses a hash function to map keys to values. Collisions are handled using methods like chaining or open addressing.

  13. Question: How does garbage collection work in Java?

    Answer: Garbage collection in Java automatically reclaims memory by identifying and removing objects no longer in use.

  14. Question: What is the difference between linear search and binary search? When is each one used?

    Answer: Linear search checks each element sequentially; binary search divides the search space. Linear is for unsorted data; binary is for sorted data.

  15. Question: Explain the concept of pointers in C/C++. Why are they useful?

    Answer: Pointers store memory addresses, allowing dynamic memory management and efficient array and string manipulation.

  16. Question: What are the advantages of using a linked list over an array?

    Answer: Linked lists have dynamic size and efficient insertions/deletions, but slower access compared to arrays.

  17. Question: What is a trie data structure, and where is it commonly used?

    Answer: A trie is a tree-like structure used for efficient retrieval of keys, commonly in autocomplete and dictionaries.

  18. Question: What is the difference between pass-by-value and pass-by-reference?

    Answer: Pass-by-value copies the actual value; pass-by-reference passes the address, affecting the original value.

  19. Question: What are lambda functions, and how are they used in Python?

    Answer: Lambda functions are anonymous functions defined with the lambda keyword, often used for short, simple operations.

  20. Question: Explain the concept of Big-O notation with examples.

    Answer: Big-O notation describes the upper bound of an algorithm's time complexity. Example: O(n) for linear search.

  21. Question: What is the difference between a heap and a binary search tree?

    Answer: A heap is a complete binary tree with a heap property; a BST has ordered nodes for efficient searching.

  22. Question: What are the best cases, average cases, and worst-case time complexity of quicksort?

    Answer: Best: O(n log n), Average: O(n log n), Worst: O(n^2).

  23. Question: What is the significance of tail recursion in optimizing algorithms?

    Answer: Tail recursion reduces memory usage by reusing stack frames, enabling optimizations.

  24. Question: Explain the use of mutexes and semaphores in multi-threaded programming.

    Answer: Mutexes prevent simultaneous access to resources; semaphores control access to a finite number of resources.

  25. 2. Algorithms

  1. Question: What is the difference between greedy algorithms and dynamic programming?

    Answer: Greedy algorithms make local optimal choices, while dynamic programming solves subproblems and uses their solutions to optimize the overall problem.

  2. Question: Explain the concept of divide and conquer in algorithm design.

    Answer: Divide and conquer breaks down problems into smaller subproblems, solving each recursively to solve the original problem.

  3. Question: What is the time complexity of the reesum problem?

    Answer: The time complexity is O(n^2).

  4. Question: What is the difference between a recursive algorithm and an iterative algorithm?

    Answer: Recursive algorithms use function calls to solve subproblems; iterative algorithms use loops to solve subproblems.

  5. Question: Explain Dijkstra's algorithm and its use case.

    Answer: Dijkstra's algorithm finds the shortest path from a source node to all other nodes in a weighted graph.

  6. Question: What are divide-and-conquer algorithms? Provide an example.

    Answer: Divide-and-conquer breaks a problem into smaller subproblems, solves them recursively, and combines their solutions. Example: Merge Sort.

  7. Question: What is the Traveling Salesman Problem (TSP), and how can it be solved using dynamic programming?

    Answer: TSP is finding the shortest path visiting all cities and returning to the start. Dynamic programming can solve it using the Held-Karp algorithm.

  8. Question: What is backtracking, and how does it work? Provide an example.

    Answer: Backtracking tries all possible solutions recursively and backtracks when a solution is invalid. Example: N-Queens problem.

  9. Question: What is the time complexity of merge sort, and why is it more efficient than bubble sort?

    Answer: Merge sort has O(n log n) complexity, making it more efficient than bubble sort's O(n^2) for large inputs.

  10. Question: Explain Floyd-Warshall algorithm and its use case in graph theory.

    Answer: Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.

  11. Question: Describe how binary search works. What are its limitations?

    Answer: Binary search divides a sorted array into halves, narrowing the search space. It requires sorted input and works only for arrays, not linked lists.

  12. Question: What is memoization, and how is it used in dynamic programming?

    Answer: Memoization caches results of expensive function calls to avoid redundant computations in dynamic programming.

  13. Question: What are NP-hard and NP-complete problems? Can NP-complete problems be solved efficiently?

    Answer: NP-hard problems are at least as hard as NP problems. NP-complete problems are in NP and as hard as any problem in NP. They lack efficient solutions.

  14. Question: How does the A search algorithm differ from Dijkstra's algorithm?*

    Answer: A* uses heuristics for faster pathfinding, while Dijkstra’s algorithm only considers edge weights.

  15. Question: What is a minimum spanning tree? Name two algorithms that can find it.

    Answer: A minimum spanning tree connects all nodes in a graph with minimal total edge weight. Algorithms: Kruskal's and Prim's.

  16. Question: What is Kruskal's algorithm, and how does it work?

    Answer: Kruskal's algorithm finds a minimum spanning tree by sorting edges by weight and adding them without forming cycles.

  17. Question: Explain the concept of topological sorting in directed acyclic graphs (DAGs).

    Answer: Topological sorting orders vertices in a DAG so that every directed edge points from earlier to later.

  18. Question: What is the difference between bubble sort and insertion sort?

    Answer: Bubble sort repeatedly swaps adjacent elements; insertion sort places each element in its correct position. Insertion sort is generally faster.

  19. Question: How does quicksort work? Explain its average-case and worst-case performance.

    Answer: Quicksort partitions an array around a pivot, sorting recursively. Average case: O(n log n); worst case: O(n^2).

  20. Question: Explain the Boyer-Moore string search algorithm.

    Answer: Boyer-Moore searches by skipping sections of text, using heuristics like bad-character and good-suffix rules for efficiency.

  21. Question: What is the difference between Bellman-Ford and Dijkstra's algorithm for shortest paths?

    Answer: Bellman-Ford handles negative weights and finds shortest paths from a single source. Dijkstra’s works only with non-negative weights.

  22. Question: Explain Prim's algorithm and its use case.

    Answer: Prim’s algorithm builds a minimum spanning tree by starting from a node and adding the smallest connecting edge iteratively.

  23. Question: What are the applications of the KMP (Knuth-Morris-Pratt) algorithm in pattern matching?

    Answer: KMP efficiently finds substrings in text using a prefix-suffix table to avoid unnecessary comparisons.

  24. 3. Object-Oriented Programming (OOP)

  25. Question: What are the four pillars of object-oriented programming?

    Answer: Encapsulation, Abstraction, Inheritance, and Polymorphism.

  26. Question: Explain the concept of encapsulation in OOP.

    Answer: Encapsulation hides internal implementation details, exposing only necessary information through public interfaces.

  27. Question: What is inheritance? Explain different types of inheritance in C++ or Java.

    Answer: Inheritance allows classes to derive properties from others. Types: Single, Multiple, Multilevel, Hierarchical, Hybrid.

  28. Question: What is polymorphism? Provide examples of compile-time and runtime polymorphism.

    Answer: Polymorphism enables one interface for different types. Compile-time: method overloading; runtime: method overriding.

  29. Question: What is encapsulation in object-oriented programming?

    Answer: Encapsulation bundles data and methods, restricting access to implementation details.

  30. Question: Explain the concept of abstraction with real-world examples.

    Answer: Abstraction hides complexity, exposing only relevant details. Example: Using a car without knowing its engine mechanics.

  31. Question: What is an abstract class? How is it different from an

    Answer: An abstract class can have method implementations; an interface only defines method signatures.

  32. Question: What is the difference between a class and an object in object-oriented programming?

    Answer: A class defines a blueprint; an object is an instanc iation of that blueprint with its own attributes and methods.

  33. Question: What is the purpose of the "this" keyword in a class?

    Answer: The "this" keyword refers to the current object, allowing access to its attributes and methods.