These Data Structures and Algorithms multiple-choice questions and their answers will help you strengthen your grip on the subject of Data Structures and Algorithms. You can prepare for an upcoming exam or job interview with these Data Structures and Algorithms MCQs.
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A. sorting data that is too large to fit into RAM
B. sorting data without the usage of a recursive implementation
C. sorting data outside a specific performance bound
A. Insertion sort
B. Selection sort
C. Quicksort
D. Bubble sort
A. Hashing
B. Sequential Search
C. Fibonacci Search
D. Binary Search
A. Stack
B. Linked List
C. Sequence
D. Array
A. Queue
B. Array
C. Stack
D. Linked List
A. Priority Queue
B. Linked List
C. Tree
D. Array
A. Lower bound
B. Upper bound
C. Midpoint
D. Range
A. Induction
B. Recursion
C. Sequencing
D. Looping
A. Yes
B. No
A. Tree
B. Array
C. Linked List
D. Priority Queue
A. One.
B. Two. One queue is used for actual storing of data and another for storing priorities.
C. Three.
D. Four.
A. Insertion sort
B. Selection sort
C. Bubble sort
D. Quicksort
A. Sequential Search
B. Hashing algorithm has been performed
C. Sorted Array
D. Unsorted Array
A. Stack requires recursive search technique; Queue does not.
B. Stack uses selection sort; Queue uses bubble sort.
C. Stack is LIFO; Queue is FIFO.
D. Stack is FIFO; Queue is LIFO.
A. Binary Tree
B. Array
C. Linked List
D. B-tree
A. Hashtable
B. Set
C. Stack
D. Queue
A. True
B. False
A. Sorting algorithms
B. Searching algorithms
C. computational complexity measurements
A. Stack
B. Binary tree
C. Queue
D. Array
A. n!
B. 2 ^ n
C. n * log(n)
D. n ^ 3
E. n ^ 2
A. False
B. True
A. Deletion of a leaf
B. Creation of a list
C. Insertion of a node
D. Deletion of a node
A. Pointers
B. Recursion
C. Binary Search
D. Hashing
A. maps each hash value to a different valid input
B. maps each valid input to a different hash value
C. not possible
A. Array
B. Binary Tree
C. B-tree
D. Stack
A. Heap
B. Linked List
C. Stack
D. Queue
A. True
B. False
A. Compiler design
B. Simulation
C. Website design
D. Graphics
A. Set
B. Stack
C. Sequence
D. Structure
A. O(N^2)
B. It depends on how both N and M vary.
C. O(N*M)
D. O(N+M)
A. O(log n)
B. O(n^3)
C. O(n^2)
D. O(1)
E. O(n)
A. Find the 2nd largest value in an array
B. Find the 2nd smallest value in an array
C. Find the maximum value in an array.
D. Find the median value in an array
A. O(n^2)
B. O(n * log n)
C. O(log n)
D. O(n)
E. O(1)
A. False
B. True
A. Ten
B. Once
C. Three
D. Two
A. O(1)
B. O(N^2)
C. O(log N)
D. O(N)
E. O(N * log N)
A. HashMap
B. Fibonacci heap
C. Sorted list
D. B-tree
E. Doubly-linked list
A. Set
B. Height
C. Size
D. Depth
A. Right Child - Parent - Left Child
B. Left Child - Parent - Right Child
C. Parent - Left Child - Right Child
D. Left Child - Right Child - Parent
A. O(n^2)
B. O(1)
C. O(log n)
D. O(n)
A. O(n^2)
B. O(n * log n)
C. O(n)
D. O(1)
E. O(n^2 * log n)
A. Database table
B. Algorithm
C. Database
D. Data Structure
A. Linear search
B. Tree search
C. Hashing
D. Binary search
A. O(NlogN)
B. O(N*N)
C. O(1)
D. O(logN)
E. O(N)
A. Root is leaf, or has between 2 & M children.
B. Data stored only on the leaves.
C. Data is stored only on the branches.
D. All leaf nodes are at the same level.
A. Insertion sort
B. Quick sort
C. Bubble sort
D. Merge sort
A. No, they can't
B. Yes, with a slight modification to the algorithm.
C. Yes, by multiplying each edge in the graph by -1, and finding the shortest-path.
A. Preorder predecessor
B. Inorder successor
C. Suborder successor
D. Inorder predecessor
A. Size
B. Height
C. Depth
D. Set
A. O(n^2)
B. O(n)
C. O(2n)
D. O(log n)
E. O(n * log n)