This section contains more frequently asked Data Structure and Algorithms Basics Multiple Choice Questions Answers in the various University level and competitive examinations.
PRACTICE IT NOW TO SHARPEN YOUR CONCEPT AND KNOWLEDGE
1. Name the node which has been generated but none of its children nodes have been generated in state space tree of backtracking method.
- Dead node
- Live node
- E-Node
- State Node
B. Live node
2. How many nodes are there in a full state space tree with n = 6?
- 65
- 64
- 63
- 32
C. 63
3. This algorithm scans the list by swapping the entries whenever pair of adjacent keys are out of desired order.
- Insertion sort.
- Bubble sort.
- Shell sort.
- Quick sort.
B. Bubble sort.
4. The notation is
- Symmetric
- Reflexive
- Transitive
- B & C only
C. Transitive
5. From the following chose the one which belongs to the algorithm paradigm other than to which others from the following belongs to.
- Minimum & Maximum problem
- Knapsack problem.
- Selection problem.
- Merge sort.
B. Knapsack problem.
6. To calculatec(i, j )’s, w( i, j)’s and r(i, j)’s; the OBST algorithm in worst case takes the following time.
- O(log n)
- O (n4)
- O (n3)
- O (n log n)
C. O (n3)
7. What is the type of the algorithm used in solving the 4 Queens problem?
- Greedy
- Dynamic
- Branch and Bound
- Backtracking.
D. Backtracking.
8. In Knapsack problem, the best strategy to get the optimal solution, where Pi, Wi is the Profit, Weight associated with each of the Xith object respectively is to
- Arrange the values Pi/Wi in ascending order
- Arrange the values Pi/Xi in ascending order
- Arrange the values Pi/Wi in descending order
- Arrange the values Pi/Xi in descending order
D. Arrange the values Pi/Xi in descending order
9. Greedy job scheduling with deadlines algorithms’ complexity is defined as
- O(N)
- Ω( n log n)
- O (n2 log n)
- O ( n log n)
A. O(N)
10. The divide and conquer merge sort algorithm’s time complexity can be defined as
- (long n)
- (n)
- Ω (n log n)
- (n log n)
D. (n log n)
