Data Structures and Algorithms Basics

1. What is 'dynamic programming' used for?

• A. Solving complex problems by breaking them into simpler overlapping subproblems ✓
• B. Writing dynamic websites
• C. Creating animations
• D. Styling applications

💡 Dynamic programming solves complex problems by breaking them into simpler, overlapping subproblems and storing results.

2. What is 'recursion' in programming?

• A. A function that calls itself ✓
• B. A type of loop
• C. A sorting algorithm
• D. A data type

💡 Recursion occurs when a function calls itself to solve smaller instances of the same problem.

3. What is 'Big O notation' used for?

• A. Describing the performance or complexity of an algorithm ✓
• B. Writing comments
• C. Declaring variables
• D. Styling code

💡 Big O notation describes how an algorithm's time or space requirements grow relative to input size.

4. What is a 'graph' in data structures?

• A. A collection of nodes connected by edges ✓
• B. A type of chart
• C. A sorting algorithm
• D. A single linked list

💡 A graph is a data structure consisting of nodes (vertices) connected by edges.

5. What is the purpose of a 'hash function'?

• A. To convert input data into a fixed-size value, typically for indexing ✓
• B. To sort an array
• C. To create a loop
• D. To compile code

💡 A hash function converts input data into a fixed-size output, typically used for indexing in hash tables.

6. What does 'hashing' typically provide in a hash table?

• A. Fast average-case lookup, insertion, and deletion ✓
• B. Guaranteed order preservation
• C. Automatic sorting
• D. Slower search than a list

💡 Hashing provides fast average-case performance for lookup, insertion, and deletion operations in a hash table.

7. What is a 'stack' data structure?

• A. A collection that follows Last In, First Out (LIFO) order ✓
• B. A collection that follows First In, First Out (FIFO) order
• C. A type of tree
• D. A type of graph

💡 A stack follows the Last In, First Out (LIFO) principle, where the most recently added item is removed first.

8. What is the time complexity of accessing an element in an array by index?

• A. O(1) ✓
• B. O(log n)
• C. O(n)
• D. O(n²)

💡 Accessing an array element by index takes constant time, O(1), since the memory address can be calculated directly.

9. What is a 'queue' data structure?

• A. A collection that follows First In, First Out (FIFO) order ✓
• B. A collection that follows Last In, First Out (LIFO) order
• C. A sorting algorithm
• D. A type of loop

💡 A queue follows the First In, First Out (FIFO) principle, where the first item added is the first removed.

10. Which sorting algorithm repeatedly steps through a list, swapping adjacent elements if they are in the wrong order?

• A. Bubble sort ✓
• B. Quick sort
• C. Merge sort
• D. Heap sort

💡 Bubble sort repeatedly compares and swaps adjacent elements to gradually sort a list.

11. What is the time complexity of searching in a balanced binary search tree?

• A. O(1)
• B. O(log n) ✓
• C. O(n)
• D. O(n²)

💡 Searching in a balanced binary search tree takes O(log n) time due to its balanced structure.

12. What is the worst-case time complexity of quicksort?

• A. O(n log n)
• B. O(n²) ✓
• C. O(log n)
• D. O(1)

💡 Quicksort's worst-case time complexity is O(n²), which occurs with poor pivot selection.

13. What is a 'binary tree'?

• A. A tree data structure where each node has at most two children ✓
• B. A tree with only one node
• C. A type of array
• D. A type of loop

💡 A binary tree is a hierarchical data structure where each node has at most two children.

14. What is a 'linked list'?

• A. A sequence of nodes where each node points to the next ✓
• B. An array with fixed size
• C. A type of loop
• D. A sorting algorithm

💡 A linked list is a data structure made of nodes, each pointing to the next node in the sequence.

15. Which sorting algorithm uses a divide-and-conquer approach with O(n log n) average time complexity?

• A. Merge sort ✓
• B. Bubble sort
• C. Selection sort
• D. Insertion sort

💡 Merge sort divides the array into halves, sorts them recursively, and merges them, achieving O(n log n) average complexity.