Invitation to Computer Science 7th Edition G Michael Schneider Judith Gersting- Test Bank

 

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Sample Test

Chapter_03_The_Efficiency_of_Algorithms   1. The properties that make better algorithms are very similar to the properties we look for when purchasing a car.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   92

 

2. The time an algorithm takes on a particular machine is the best way for comparing two algorithms that do the same task.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   96

 

3. If we were to run the sequential search algorithm many times, with random input values occurring at various places in the list, we would find the average number of comparisons done to be approximately n/2.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   99

 

4. Sequential search is an order-n algorithm in the average case.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   101

 

5. The sequential search and selection sort algorithms are different methods to get the same thing done.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   102

 

6. The selection sort algorithm can recognize whether or not the list is already sorted at the beginning.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   107

 

7. If an Θ(n2) algorithm and an Θ(n) algorithm exist for the same task, then for large enough n, the Θ(n2) algorithm does more work and takes longer to execute, regardless of the constant factors for peripheral work.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   112

 

8. Given a sorted list, the sequential search algorithm is more efficient than the binary search.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   123

 

9. Binary search uses significantly more space than sequential search.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   129

 

10. No one has yet found a solution algorithm that works in polynomial time, but neither has anyone proved that such an algorithm does not exist.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   135

 

11. First and foremost, we expect elegance from our algorithms. _________________________ ANSWER:   False – correctness POINTS:   1 REFERENCES:   92

 

12. In the sequential search algorithm, the worst case occurs when the value being searched for is the first value in the list. _________________________ ANSWER:   False – best POINTS:   1 REFERENCES:   98

 

13. With sequential search, the bigger the list of items, the more work that must be done to search it. _________________________ ANSWER:   True POINTS:   1 REFERENCES:   99

 

14. The shuffle-left algorithm is not space-efficient. _________________________ ANSWER:   False – is POINTS:   1 REFERENCES:   119

 

15. A collection of nodes and connecting edges is called a(n) vector. _________________________ ANSWER:   False – graph POINTS:   1 REFERENCES:   133

 

16. ____ is the algorithmic equivalence of style.   a. Efficiency b. Elegance   c. Aesthetics d. Complexity   ANSWER:   b POINTS:   1 REFERENCES:   94

 

17. ____ involves the fixing of errors that are uncovered through repeated usage with different input values.   a. Program maintenance b. Recycling   c. Data cleanup d. Garbage collection   ANSWER:   a POINTS:   1 REFERENCES:   94

 

18. ____ are useful for rating one machine against another and for rating how sensitive a particular algorithm is with respect to variations in input on one particular machine.   a. Time trials b. Benchmarks   c. Comparison times d. Intensive tests   ANSWER:   b POINTS:   1 REFERENCES:   96

 

19. The study of the efficiency of algorithms is called the ____ of algorithms.   a. design b. analysis   c. implementation d. testing   ANSWER:   b POINTS:   1 REFERENCES:   97

 

20. In the sequential search algorithm, the minimum amount of work is done if the value being searched for is the ____ value in the list.   a. first b. second   c. middle d. last   ANSWER:   a POINTS:   1 REFERENCES:   98

 

21. The ____ case of an algorithm requires the least work.   a. best b. worst   c. smallest d. largest   ANSWER:   a POINTS:   1 REFERENCES:   98

 

22. In the sequential search algorithm, the worst case occurs when the value being searched for is the ____ value in the list.   a. first b. second   c. middle d. last   ANSWER:   d POINTS:   1 REFERENCES:   98

 

23. Placing a list of items into alphabetical or numerical order is called ____.   a. simplifying b. searching   c. sorting d. pattern matching   ANSWER:   c POINTS:   1 REFERENCES:   103

 

24. The ____ sort algorithm performs the task of sorting a list by growing a sorted subsection of the list from the back to the front.   a. selection b. sequential   c. shuffle-left d. binary   ANSWER:   a POINTS:   1 REFERENCES:   103

 

25. Selection sort is a(n) ____ algorithm in all cases.   a. Θ(1) b. Θ(n)   c. Θ(2n) d. Θ(n2)   ANSWER:   d POINTS:   1 REFERENCES:   111

 

26. Sequential search is a(n) ____ algorithm in the worst case.   a. Θ(1) b. Θ(n)   c. Θ(2n) d. Θ(n2)   ANSWER:   b POINTS:   1 REFERENCES:   111

 

27. Part of the job of program ____ is to make clear any assumptions or restrictions about the input size the program was designed to handle.   a. design b. implementation   c. documentation d. maintenance   ANSWER:   c POINTS:   1 REFERENCES:   114

 

28. The shuffle-left algorithm is a(n) ____ algorithm in the worst case.   a. Θ(1) b. Θ(n)   c. Θ(2n) d. Θ(n2)   ANSWER:   d POINTS:   1 REFERENCES:   119

 

29. The copy-over algorithm is ____ in time efficiency in the worst case.   a. Θ(1) b. Θ(n)   c. Θ(2n) d. Θ(n2)   ANSWER:   b POINTS:   1 REFERENCES:   119

 

30. The worst case in binary search occurs ____.   a. when the object to be searched is in the middle of the list   b. when the object to be searched is at the end of the list   c. when the object to be searched is at the beginning of the list   d. when the object to be searched is not in the list   ANSWER:   d POINTS:   1 REFERENCES:   128

 

31. Binary search does ____ comparisons in the worst case.   a. Θ(1) b. Θ(lg n)   c. Θ(n) d. Θ(n2)   ANSWER:   b POINTS:   1 REFERENCES:   128

 

32. Θ(lg n), Θ(n) and Θ(n2) are ____ in the amount of work they do as n increases.   a. restricted b. useful   c. polynomially bounded d. exponential   ANSWER:   c POINTS:   1 REFERENCES:   132

 

33. An ____ algorithm is called an exponential algorithm.   a. Θ(lg n) b. Θ(n)   c. Θ(n2) d. Θ(2n)   ANSWER:   d POINTS:   1 REFERENCES:   134

 

34. Problems for which no known polynomial solution algorithm exists are sometimes approached via ____ algorithms.   a. alternative b. intractable   c. polynomial d. approximation   ANSWER:   d POINTS:   1 REFERENCES:   136

 

35. A surprising number of problems fall into the “____” category.   a. suspected intractable b. approximation algorithm   c. bin-packing d. declared intractable   ANSWER:   a POINTS:   1 REFERENCES:   136

 

36. ____________________ is the term to describe an algorithm’s careful use of resources. ANSWER:   Efficiency POINTS:   1 REFERENCES:   95

 

37. The number of comparisons done by the selection sort algorithm does not grow at the same rate as the problem size n, instead it grows at approximately the ____________________ of that rate. ANSWER:   square POINTS:   1 REFERENCES:   109

 

38. The converging-pointers algorithm is Θ(n) in the ____________________ case. ANSWER:   worst POINTS:   1 REFERENCES:   122

 

39. A(n) ____________________ is a path through a graph that begins and ends at the same node and goes through all other nodes exactly once. ANSWER:   Hamiltonian circuit POINTS:   1 REFERENCES:   133

 

40. ____________________ problems are solvable, but the solution algorithms all require so much work as to be virtually useless. ANSWER:   Intractable POINTS:   1 REFERENCES:   135

 

41. What is the definition of order of magnitude n? ANSWER:   Anything that varies as a constant times n (and whose graph follows the basic shape of n) is said to be order of magnitude n. POINTS:   1 REFERENCES:   101 TOPICS:   Critical Thinking

 

42. Explain the sentence: The selection sort algorithm not only does comparisons; it also does exchanges. ANSWER:   Even if the largest number in the unsorted section of a list is already at the end of the unsorted section, the selection sort algorithm exchanges this number with itself. Therefore, the algorithm does n exchanges, one for each position in the list to put the correct value in that position. With every exchange, the marker gets moved. (However, the work contributed by exchanges and market moving is so much less than the amount contributed by comparisons that it can be ignored.) POINTS:   1 REFERENCES:   107 TOPICS:   Critical Thinking

 

43. If an algorithm is more time efficient and less space efficient, what is this called? ANSWER:   This choice is called the time/space trade-off, in which you gain something by giving up something else. POINTS:   1 REFERENCES:   120 TOPICS:   Critical Thinking

 

44. What is the logarithm of n to the base 2? ANSWER:   The number of times a number n can be cut in half and not go below 1 POINTS:   1 REFERENCES:   127 TOPICS:   Critical Thinking

 

45. How do you approach problems for which no known polynomial solution algorithm exists? ANSWER:   Problems for which no known polynomial solution algorithm exists are sometimes approached via approximation algorithms. These algorithms don’t solve the problem, but they provide a close approximation to a solution. POINTS:   1 REFERENCES:   136 TOPICS:   Critical Thinking

 

46. Discuss at length the “people aspect” of the practical considerations for the development of algorithms. Include the concepts of program maintenance and ease of understanding in your response. ANSWER:   The practical considerations for computer science arise because the algorithms developed are executed in the form of computer programs running on real computers to solve problems of interest to real people. With regard to the “people aspect”: A computer program is seldom written to be used only once to solve a single instance of a problem. It is written to solve many instances of that problem with many different input values, just as the sequential search algorithm would be used many times with different lists of names and different target NAME values. Furthermore, the problem itself does not usually “stand still.” If the program is successful, people will want to use it for slightly different versions of the problem, which means they will want the program slightly enhanced to do more things. Therefore, after a program is written, it needs to be maintained, both to fix any errors that are uncovered through repeated usage with different input values, and to extend the program to meet new requirements. A great deal of time and money are devoted to program maintenance. The person who has to modify a program, either to correct errors or to expand its functionality, often is not the person who wrote the original program. To make program maintenance as easy as possible, the algorithm the program uses should be easy to understand. Ease of understanding, clarity, “ease of handling”—whatever you want to call it—is a highly desirable characteristic of an algorithm. POINTS:   1 REFERENCES:   93-94 TOPICS:   Critical Thinking

 

47. Discuss at length the measurement of the time and space consumed by an algorithm in order to determine its efficiency. ANSWER:   To determine whether an algorithm is efficient, space efficiency can be judged by the amount of information the algorithm must store in the computer’s memory to do its job, in addition to the initial data on which the algorithm is operating. If it uses only a few extra memory locations while processing the input data, the algorithm is relatively space efficient. If the algorithm requires almost as much additional storage as the input data itself takes up, or even more, then it is relatively space inefficient. To measure the time efficiency of an algorithm, consider a sequential search algorithm that looks up a name in a telephone directory where the names are not arranged in alphabetical order. How about running the algorithm on a real computer and timing it to see how many seconds (or maybe what small fraction of a second) it takes to find a name or announce that the name is not present? The difficulty with this approach is that there are three factors involved, each of which can affect the answer to such a degree as to make whatever number of seconds we come up with rather meaningless. 1. On what computer will we run the algorithm? Shall we use a modest laptop or a supercomputer capable of doing many trillions of calculations per second? 2. What telephone book (list of names) will we use: New York City or Yeehaw Junction, Florida? 3. What name will we try to find? What if we pick a name that happens to be first in the list? What if it happens to be last in the list? Simply timing the running of an algorithm is more likely to reflect machine speed or variations in input data than the efficiency (or lack thereof) of the algorithm itself. This is not to say that you can’t obtain meaningful information by timing an algorithm. For example, using the same input data (searching for Karlenski, say, in the New York City phone book) and timing the algorithm on different machines gives a comparison of machine speeds because the task is identical. Using the same machine and the same list of names, but searching for different names, gives an indication of how the choice of NAME affects the algorithm’s running time on that particular machine. This type of comparative timing is called benchmarking. Benchmarks are useful for rating one machine against another and for rating how sensitive a particular algorithm is with respect to variations in input on one particular machine. However, what we mean by an algorithm’s time efficiency is an indication of the amount of “work” required by the algorithm itself. It is a measure of the inherent efficiency of the method, independent of the speed of the machine on which it executes or the specific input data being processed. Is the amount of work an algorithm does the same as the number of instructions it executes? Not all instructions do the same things, so perhaps they should not all be “counted” equally. Some instructions are carrying out work that is fundamental to the way the algorithm operates, whereas other instructions are carrying out peripheral tasks that must be done in support of the fundamental work. To measure time efficiency, we identify the fundamental unit (or units) of work of an algorithm and count how many times the work unit is executed. POINTS:   1 REFERENCES:   95-96 TOPICS:   Critical Thinking

 

48. Given the data cleanup problem described in the text, write the pseudocode for the converging-pointers algorithm for data cleanup. ANSWER:   1. Get values for n and the n data items 2. Set the value of legit to n 3. Set the value of left to 1 4. Set the value of right to n 5. While left is less than right do Steps 6 through 10 6. If the item at position left is not 0 then increase left by 1 7. Else (the item at position left is 0) do Steps 8 through 10 8. Reduce legit by 1 9. Copy the item at position right into position left 10. Reduce right by 1 11. If the item at position left is 0, then reduce legit by 1 12. Stop POINTS:   1 REFERENCES:   121 TOPICS:   Critical Thinking

 

49. Explain the term “brute force” as it applies to algorithms. ANSWER:   The algorithm described here for testing an arbitrary graph for Hamiltonian circuits is an example of a brute force algorithm—one that beats the problem into submission by trying all possibilities. In Chapter 1, we described a brute force algorithm for winning a chess game; it consisted of looking at all possible game scenarios from any given point on and then picking a winning one. This is also an exponential algorithm. Some very practical problems have exponential solution algorithms. For example, an email message that you send over the Internet is routed along the shortest possible path through intermediate computers from your mail server computer to the destination mail server computer. An exponential algorithm to solve this problem would examine all possible paths to the destination and then use the shortest one. As you can imagine, the Internet uses a better (more efficient) algorithm than this one! POINTS:   1 REFERENCES:   135 TOPICS:   Critical Thinking

 

50. Discuss at length both the best and worst case of the pattern-matching algorithm. ANSWER:   Both the best case and the worst case of this algorithm can occur when the pattern is not in the text at all. The difference hinges on exactly how the pattern fails to be in the text. The best case occurs if the first character of the pattern is nowhere in the text, as in Text: KLMNPQRSTX Pattern: ABC In this case, n – m + 1 comparisons are required, trying (unsuccessfully) to match P1 with T1, T2, . . . , Tn–m+1 in turn. Each comparison fails, and the algorithm slides the pattern forward to try again at the next position in the text. The maximum amount of work is done if the pattern almost occurs everywhere in the text. Consider, for example, the following case: Text: AAAAAAAAA Pattern: AAAB Starting with T1, the first text character, the match with the first pattern character is successful. The match with the second text character and the second pattern character is also successful. Indeed m – 1 characters of the pattern match with the text before the mth comparison proves a failure. The process starts over from the second text character, T2. Once again, m comparisons are required to find a mismatch. Altogether, m comparisons are required for each of the n – m + 1 starting positions in the text. Another version of the worst case occurs when the pattern is found at each location in the text, as in Text: AAAAAAAAA Pattern: AAAA This results in the same comparisons that are done for the other worst case. The only difference is that the comparison of the last pattern character is successful. POINTS:   1 REFERENCES:   130-131 TOPICS:   Critical Thinking

 

Chapter_05_Computer_Systems_Organization     1. The branch of computer science that studies computers in terms of their major functional units and how they work is known as computer organization.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   222

 

2. Both RAM and ROM are memory chips into which information has been prerecorded during manufacture.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   226

 

3. Memory locations are stored in row major order.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   232

 

4. As computers become faster, memory access speeds are keeping pace.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   235

 

5. The principle of locality states that when the computer uses something, it will probably use it again very soon.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   236

 

6. In a two-level memory hierarchy, when the computer needs a piece of information, it looks in RAM first, then cache memory.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   237

 

7. Registers can be accessed much more quickly than random access memory.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   245

 

8. The instructions that can be decoded and executed by the control unit of a computer are represented in machine language.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   250

 

9. The set of all operations that can be executed by a processor is called its I/O set.   a. True   b. False   ANSWER:   False POINTS:   1 REFERENCES:   251

 

10. The Von Neumann bottleneck is the inability of the sequential one-instruction-at-a-time computer Von Neumann model to handle today’s large-scale problems.   a. True   b. False   ANSWER:   True POINTS:   1 REFERENCES:   267