1. Two main measures for the efficiency of an algorithm are
A. Processor and memory
B. Complexity and capacity
C. Time and space
D. Data and space
2. The time factor when determining the efficiency of algorithm is measured by
A. Counting microseconds
B. Counting the number of key operations
C. Counting the number of statements
D. Counting the kilobytes of algorithm
3. The space factor when determining the efficiency of algorithm is measured by
A. Counting the maximum memory needed by the algorithm
B. Counting the minimum memory needed by the algorithm
C. Counting the average memory needed by the algorithm
D. Counting the maximum disk space needed by the algorithm
4. Which of the following case does not exist in complexity theory
A. Best case
B. Worst case
C. Average case
D. Null case
5. The Worst case occur in linear search algorithm when
A. Item is somewhere in the middle of the array
B. Item is not in the array at all
C. Item is the last element in the array
D. Item is the last element in the array or is not there at all
6. The Average case occur in linear search algorithm
A. When Item is somewhere in the middle of the array
B. When Item is not in the array at all
C. When Item is the last element in the array
D. When Item is the last element in the array or is not there at all
7. The complexity of the average case of an algorithm is
A. Much more complicated to analyze than that of worst case
B. Much more simpler to analyze than that of worst case
C. Sometimes more complicated and some other times simpler than that of worst case
D. None or above
8. The complexity of linear search algorithm is
A. O(n)
B. O(log n)
C. O(n^2)
D. O(n log n)
9. The complexity of Binary search algorithm is
A. O(n)
B. O(logn)
C. O(n^2)
D. O(n log n)
10. The complexity of Bubble sort algorithm is
A. O(n)
B. O(log n)
C. O(n^2)
D. O(n log n)
11. The complexity of merge sort algorithm is
A. O(n)
B. O(log n)
C. O(n^2)
D. O(n log n)
12. The indirect change of the values of a variable in one module by another module is called
A. internal change
B. inter-module change
C. side effect
D. side-module update
13. Which of the following data structure is not linear data structure?
A. Arrays
B. Linked lists
C. Both of above
D. None of above
14. Which of the following data structure is linear data structure?
A. Trees
B. Graphs
C. Arrays
D. None of above
15. The operation of processing each element in the list is known as
A. Sorting
B. Merging
C. Inserting
D. Traversal
16. Finding the location of the element with a given value is:
A. Traversal
B. Search
C. Sort
D. None of above
17. Arrays are best data structures
A. for relatively permanent collections of data
B. for the size of the structure and the data in the structure are constantly changing
C. for both of above situation
D. for none of above situation
18. Linked lists are best suited
A. for relatively permanent collections of data
B. for the size of the structure and the data in the structure are constantly changing
C. for both of above situation
D. for none of above situation
19. Each array declaration need not give, implicitly or explicitly, the information about
A. the name of array
B. the data type of array
C. the first data from the set to be stored
D. the index set of the array
20. The elements of an array are stored successively in memory cells because
A. by this way computer can keep track only the address of the first element and the addresses of other elements can be calculated
B. the architecture of computer memory does not allow arrays to store other than serially
C. both of above
D. none of above
21. What do you mean by Amortized Analysis?
Amortized analysis finds the average running time per operation over a worst case
sequence of operations. Amortized analysis differs from average-case performance in that
probability is not involved; amortized analysis guarantees the time per operation over
worst-case performance.
22. What are important problem types? (or) Enumerate some important types of
problems.
1. Sorting 2. Searching
3. Numerical problems 4. Geometric problems
5. Combinatorial Problems 6. Graph Problems
7. String processing Problems
23. Name some basic Efficiency classes
1. Constant 2. Logarithmic 3. Linear 4. nlogn
5. Quadratic 6. Cubic 7. Exponential 8. Factorial
24. What are algorithm design techniques?
Algorithm design techniques ( or strategies or paradigms) are general approaches to solving problems algorithmatically, applicable to a variety of problems from different areas of computing. General design techniques are:
(i) Brute force (ii) divide and conquer
(iii) decrease and conquer (iv) transform and concquer
(v) greedy technique (vi) dynamic programming
(vii) backtracking (viii) branch and bound
25. How is an algorithm’s time efficiency measured?
Time efficiency indicates how fast the algorithm runs. An algorithm’s time
efficiency is measured as a function of its input size by counting the number of times its basic operation (running time) is executeD. Basic operation is the most time consuming operation in the algorithm’s innermost loop.
26. What is Big ‘Oh’ notation?
A function t(n) is said to be in O(g(n)), denoted t(n) O(g(n)) , if t(n) is bounded aboveby some constant multiple of g(n) for all large n, i.e., if there exist some positive constantc and some nonnegative integers n0 such that
t(n) ≤ cg(n) for all n≥ n0
27. What is an Activation frame?
It is a storage area for an invocation of recursive program (parameters, local
variables, return address/value etC.). Activation frame allocated from frame stack pointed by frame pointer.
28. Define order of an algorithm
Measuring the performance of an algorithm in relation with the input size n is known as order of growth.
29. What is recursive call?
Recursive algorithm makes more than a single call to itself is known as recursive call. An algorithm that calls itself is direct recursive.An algorithm”A” is said to be indirect recursive if it calls another algorithm which in turn calls “A”.
30. What do you mean by stepwise refinement?
In top down design methodology the problem is solved in sequence (step by step)is known as stepwise refinement.
31. How is the efficiency of the algorithm defined?
The efficiency of an algorithm is defined with the components.
(i) Time efficiency -indicates how fast the algorithm runs
(ii) Space efficiency -indicates how much extra memory the algorithm needs
32. Define direct recursive and indirect recursive algorithms.
Recursion occurs where the definition of an entity refers to the entity itself.
Recursion can be direct when an entity refers to itself directly or indirect when it refers to other entities which refers to it. A (Directly) recursive routine calls itself. Mutually recursive routines are an example of indirect recursion. A (Directly) recursive data type
contains pointers to instances of the data type.
33. Which data structure allows deleting data elements from front and inserting at rear?
A. Stacks
B. Queues
C. Deques
D. Binary search tree
34. Identify the data structure which allows deletions at both ends of the list but insertion at only one end.
A. Input-restricted deque
B. Output-restricted deque
C. Priority queues
D. None of above
35. Which of the following data structure is non-linear type?
A. Strings
B. Lists
C. Stacks
D. None of above
36. Which of the following data structure is linear type?
A. Strings
B. Lists
C. Queues
D. All of above
37. To represent hierarchical relationship between elements, which data structure is suitable?
A. Deque
B. Priority
C. Tree
D. All of above
38. A binary tree whose every node has either zero or two children is called
A. Complete binary tree
B. Binary search tree
C. Extended binary tree
D. None of above
39. The depth of a complete binary tree is given by
A. Dn = n log2n
B. Dn = n log2n+1
C. Dn = log2n
D. Dn = log2n+1
40. When representing any algebraic expression E which uses only binary operations in a 2-tree,
A. the variable in E will appear as external nodes and operations in internal nodes
B. the operations in E will appear as external nodes and variables in internal nodes
C. the variables and operations in E will appear only in internal nodes
D. the variables and operations in E will appear only in external nodes
41. A binary tree can easily be converted into q 2-tree
A. by replacing each empty sub tree by a new internal node
B. by inserting an internal nodes for non-empty node
C. by inserting an external nodes for non-empty node
D. by replacing each empty sub tree by a new external node
42. When converting binary tree into extended binary tree, all the original nodes in binary tree are
A. internal nodes on extended tree
B. external nodes on extended tree
C. vanished on extended tree
D. None of above
43. The post order traversal of a binary tree is DEBFCA. Find out the pre order traversal
A. ABFCDE
B. ADBFEC
C. ABDECF
D. ABDCEF
44. Which of the following sorting algorithm is of divide-and-conquer type?
A. Bubble sort
B. Insertion sort
C. Quick sort
D. All of above
45. An algorithm that calls itself directly or indirectly is known as
A. Sub algorithm
B. Recursion
C. Polish notation
D. Traversal algorithm
46. In a binary tree, certain null entries are replaced by special pointers which point to nodes higher in the tree for efficiency. These special pointers are called
A. Leaf
B. branch
C. path
D. thread
47. The in order traversal of tree will yield a sorted listing of elements of tree in
A. Binary trees
B. Binary search trees
C. Heaps
D. None of above
48. In a Heap tree
A. Values in a node is greater than every value in left sub tree and smaller than right sub tree
B. Values in a node is greater than every value in children of it
C. Both of above conditions applies
D. None of above conditions applies
49. In a graph if e=[u, v], Then u and v are called
A. endpoints of e
B. adjacent nodes
C. neighbors
D. all of above
50. A connected graph T without any cycles is called
A. a tree graph
B. free tree
C. a tree
D. All of above
51. In a graph if e=(u, v) means
A. u is adjacent to v but v is not adjacent to u
B. e begins at u and ends at v
C. u is processor and v is successor
D. both b and c
52. If every node u in G is adjacent to every other node v in G, A graph is said to be
A. isolated
B. complete
C. finite
D. strongly connected
