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Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 1 of 12 
 
 
 
This exam paper must not be removed from the venue 
 
 
School of Information 
Technology and Electrical 
Engineering 
EXAMINATION 
Semester One Final Examinations, 2017 
INFS7901 Database Principles 
This paper is for St Lucia Campus students. 
Examination Duration: 90 minutes 
Reading Time: 10 minutes 
Exam Conditions: 
This is a School Examination 
This is a Closed Book Examination - specified materials permitted 
During reading time - write only on the rough paper provided 
This examination paper will be released to the Library 
Materials Permitted In The Exam Venue: 
(No electronic aids are permitted e.g. laptops, phones) 
Calculators - Any calculator permitted - unrestricted 
One A4 sheet of handwritten or typed notes double sided is permitted 
Materials To Be Supplied To Students: 
1 x 14 Page Answer Booklet 
1 x Multiple Choice Answer Sheet 
Instructions To Students: 
Additional exam materials (eg. answer booklets, rough paper) will be 
provided upon request. 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
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Question 3. (10 Marks) SQL 
 
Answer the following questions using queries in SQL. 
 
For this question, we will be using the schema that was introduced in Question 2: 
• Student (sID, sName, age, GPA, sizeHS) 
• College (cName, state, enrolment) 
• Apply (sID, cName, major, decision) 
 
 
a) (4 marks) Find IDs of students that are not from the smallest high school. 
 
 
b) (6 marks) Find the id(s) and name(s) of the youngest student(s) that have been 
accepted in the CS major in each of the states. 
 
 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 5 of 12 
Module 2 
Question 4. (10 Marks) Asymptotic Analysis 
 
Assume that you are asked to make a recommendation between two algorithms (A and 
B) that accomplish exactly the same task. After measuring the run times for various 
inputs sizes, you determine that the average running time (for input of size n) of algorithm 
A is TA(n) = 3n2, and the average running time of algorithm B is TB(n) = 4n + 100. 
 
a) (2 marks) For n =10, which algorithm would run faster? Explain your answer 
 
 
 
 
 
 
 
 
 
b) (2 marks) For n = 1000, which algorithm would run faster? Explain your 
answer 
 
 
 
 
 
c) (2 marks) Determine the big-o notation of each of the algorithms. 
 
 
 
 
 
 
 
 
 
d) (4 marks) Which algorithm would you recommend? Refer to your answers 
from part a, b, and c in your recommendation. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
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Question 5. (6 Marks) Sorting 
 
In class, a variety of sorting algorithms were examined. For parts (a) to (c), choose 
the best algorithm from the ones we have examined. Justify your answer (in a 
sentence or two) referencing the running time of the algorithms. 
 
a) (2 marks) You are a data analyst and you are trying to put an input of a few 
million numbers into order. You are aware that the input is almost sorted and 
there may only be a few numbers that you need to move around. Which 
sorting algorithm would you use? 
 
b) (2 marks) You are sorting a few million numbers in a cloud environment in 
which read operations take O(1), constant time, and write operations take 
O(n), linear time. Which sorting algorithm would you use? 
 
c) (2 marks) You are part of an analysis team that is operating under very strict 
and sensitive timelines, so you want to be able to guarantee that the sorting 
algorithm that you use runs in O(n log n) in the absolute worst case. What 
sorting algorithm would you use? 
 
Question 6. (4 Marks) Sorting 
 
An array is supplied to the partition routine used by quicksort(). After the very first call 
to partition() the array contains the following numbers.: 
 
what number(s) in the array can be the pivot? Note the actual number(s) is wanted, 
not the position of the number(s) in the array. 
 
 
 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
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Question 7. (10 Marks) Binary Search Trees 
 
This question makes reference to the following Binary Search Tree: 
 
 
a) (1 mark) What is the height of this tree? 
 
b) (1 mark) Which node is the root 
node? 
 
 
c) (1 mark) What is the depth of node 
19? 
 
 
d. (1 mark) Which nodes are leaves? 
 
e. (2 marks) When building a tree using a sequence of insertions (i.e. the insertion 
technique from class) which one of the following is the appropriate sequence of 
insertions to build the tree? 
I. 23, 60, 17, 80, 40, 19, 10, 90, 21, 11, 6, 81 
II. 23, 81, 90, 80, 40, 60, 21, 19, 11, 6, 10, 17 
III. 23, 17, 10, 6, 11, 19, 21, 40, 60, 80, 90, 81 
IV. 6, 11, 10, 17, 21, 19, 23, 40, 60, 81, 90, 80 
V. 6, 10, 11, 17, 19, 21, 23, 40, 60, 80, 81, 90 
VI. 6, 11, 10, 21, 19, 17, 40, 81, 90, 80, 60, 23 
VII. 81, 6, 11, 21, 90, 10, 19, 40, 80, 17, 60, 23 
VIII. 81, 90, 21, 11, 6, 80, 40, 19, 10, 60, 17, 23 
 
 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 9 of 12 
 
Question 8. (6 Marks) Hashing 
 
Consider a hash table of size 7 with hash function h(k) = k mod 7. Draw the contents 
of the table after inserting, in the given order, the following values into the table: 9, 16, 
27, 62, and 2. 
a) (3 marks) When chaining is used to resolve collisions 
 
Question 9. (4 Marks) Hashing 
 
For good hashing performance, we need to have a good hash function. Can we create an ideal 
hash function that would work well in all problems independent of the dataset? If so explain 
how, and if not explain why? 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 10 of 12 
 
Module 3 
Question 10. (10Marks) Indexing 
 
For this question, we will be using the schema that was introduced in Question two: 
• Student (sID, sName, age, GPA, sizeHS) 
• College (cName, state, enrolment) 
• Apply (sID, cName, major, decision) 
Suppose you know that the following queries are the most common queries in the 
database and that all are roughly equivalent in frequency and importance: 
1. Find name, age, GPA, and the high school size of student based on their SID 
2. Find name of colleges in each state 
3. Find students IDS with GPA higher than 3.6 
4. Find SID of students that have applied to each college 
These queries occur much more frequently than updates, so you should build whatever 
indexes you need to speed up these queries. However, you should not build any un- 
necessary indexes, as updates will occur (and would be slowed down by unnecessary 
indexes). Given this information, decide which attributes should be indexed and whether 
each index should be a clustered index or an unclustered index. Assume that both B+ 
trees and hashed indexes are supported by the DBMS and that both single- and multiple- 
attribute index search keys are permitted. 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 11 of 12 
Question 11. (10 Marks) Query Optimization 
 
For this question, we will be using the schema that was introduced in Question two: 
• Student (sID, sName, age, GPA, sizeHS) 
• College (cName, state, enrolment) 
• Apply (sID, cName, major, decision) 
 
Consider the following query 
 
Select s.sID 
from Student s, College c, Apply a 
Where s.sID = a.sID and c.cName=a.cName and 
GPA>3.5 and c.cName='stanford' and decision='Y' 
 
a) (5 marks) Draw the initial query tree for this query and explain briefly why this 
plan is considered inefficient. 
 
Semester One Final Examinations, 2017 INFS7901 Database Principles 
Page 12 of 12 
b) (5 marks) Transform the initial query into an equivalent final query tree that is 
efficient to execute. 
END OF EXAMINATION 
 
 
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