THE UNIVERSITY OF NEW SOUTH WALES School of Computer Science and Engineering
Final Examination– Term1, 2021 30th April, 2021
COMP9321 Data Service Engineering
Total Exam Mark: 40
Total Number of Questions: 20 + 10
Exam Duration: 2 Hours +15 minutes (reading and submitting)
**** IMPORTANT NOTICE****
There are Two parts in this exam paper: Part A – Multiple Choice Questions, Part B – Written Answer Questions. Plan your time wisely and attempt to complete all parts.
You may submit your solutions as many times as you like. The last submission ONLY will be marked. Questions (and sub-questions) are not worth equal marks. Answer all questions.
For multiple choice questions select the response which best answers the question. Keep your written answers clear and coherent. Messy or irrelevant answers will not be marked.
The Answers need to be according to your own effort and in your own words. If you do not follow these instructions, you will get zero marks for the exam and a possible charge of academic misconduct.
PartA: Multiple Choice Questions (Total 10 Marks)
Use Moodle Quiz to Answer all the 20 Questions. The last submission is going to be marked. Make
sure you click submit at the end so the submission will be considered.
https://moodle.telt.unsw.edu.au/mod/quiz/view.php?id=3789585
PartB: Written Answer Questions (Total 30 Marks)
The written Answer Questions Paper is to be submitted using Give System as a PDF file named z{id}.pdf
Question1 (2 marks):
In your own words, explain the principle of HATEOAS (Hypermedia As The Engine Of Application State) enrich your explanation with an example.
Question2 (2 marks)
In your own words, explain when we should consider Deep Learning versus when should we consider a more traditional machine learning algorithm (e.g., kNN, decision trees, linear regression…etc).
Question3 (1 mark)
Mention 3 security consideration you may prioritize when securing your RESTful API. In your own words, explain one of them with an example.
Question4 (2 marks)
For the Data snippet below, list
the data quality problem(s). Mention the record(s) and the field(s).
Student ID
Name
Program
Cohort
Gender
Age
Date of Birth
City
1234 John Smith
1122 Jane Doe
1234 Smith, John
3234 Jack Sparrow
5223 Ada Wong
Question5: ( 2 marks)
3344 3322 3344 2211 N/A
UGRD PGRD UGRD postgrad None
M F M M F
21 20 21 21 -19
23-04- Sydney 1999
01-18- Syddey 2000
23-04- Brisbane 1999
17-01- Australia 2999
7-11- Sydney 2001
An organization has two datasets one for devices, their location, the operator, and quality tests; and the other is about the technical support tickets opened for each device. The organization want to draw some insights in regard to the opened support tickets for each device and the relation with when the device was quality tested and who is the operator.
In the light of the datasets snippets shown below, what pre-processing (cleansing and manipulation) is needed to make sure that the organization can conduct the required task. Explain each step in the light of the datasets provided. You can use Python code, pseudo code, or you can explain as a series of steps. In the case of using code there is no need to preserve the syntax but it is a MUST to include proper commenting to explain each step. Be advised that the organization is low on resources (e.g., storage), so that need to be considered in the pre-processing.
Dataset 1
Device ID
Quality Tested Date/Time
Location
Operator
B1834 B9872 N2543 n/a M4328 B9872
2019-01-16:23:59:12 2019-01-03:09:15:17 2019-01-27:06:39:01 2019-01-18:06:39:01 2019-03-27:09:30:01 2019-01-29:08:19:17
K17-401-08 K17-401-08 K17-502-12 NaN K17-401-09 K17-401-08
Albert Albert Jill NaN Chris Albert
Dataset 2
Device ID
Support Ticket Date/time
Ticket Handled by
B1834 N2543 M4328 B9872 M4328
2019-21-01:11:59:12 AM 2019-01-03:03:39:01 PM 2019-23-05:01:30:01 PM 2019-16-03:08:19:17 AM 2019-23-05:01:30:01 PM
Morty Morty Morty Morty –
Question6: (3 marks)
Suppose a shop owner wants to divide her customers into different groups. She has the number of purchases they made in the last year and based on the number of purchases, she wants to segment them into groups. There is no fixed target here as to how many groups to have. the shop owner does not know what type of customers should be assigned to which group. Below is a data sample.
Shopper ID Purchases Made A 18 B7
C 22
D 12 E 24
A) What Machine learning Algorithm are you going to use to solve this problem? Why?
B) Illustrate the calculation steps of how the algorithm is going to work and groups are going to
be formulated. Explaining each step.
C) Explain how you will determine the number of Groups eventually
Question7: (2 Marks)
Consider the following confusion matrix Current Answer
True Predicted Answer True 8
Predicted Answer False 12
Current Answer False
2
11
For the above “confusion matrix” what is the precision, recall and F1-score? Explain in your own words when it would be more suitable to prioritize precession over recall and vice versa.
Question8: (2 Marks)
You have a coffee service where you handle coffee orders (drink type, size, number of shots):
A. Consider the following HTTP request invoking a POST method of the Coffee RESTful API. Write down the content of the HTTP response that you would return as the result.
POST /orders
HTTP/1.1
Host: api.coffeehouse.com Content-Type: application/json {
“drink” : ”latte”,
“size” : ”small”,
“shots” : 2
}
B. In the light of part A of the question, consider the following HTTP request invoking a GET method of the Coffee RESTful API. Write down the content of the HTTP response that you would return as the result. Do not be concerned about the specific content as long as it is relevant.
GET /orders?order_by=+id&filter=id,drink HTTP/1.1
Host: api.coffeehouse.com
Content-Type: application/json Question9: (5 marks)
You have taken the role of cyber security analyst in an organization. You are considering utilizing your data service engineering knowledge to help the team. You have access to a vulnerability database from a security service provider including information about software vulnerabilities discovered, the type of the vulnerability, the software that this vulnerability has affected, the version affected with the vulnerability, the severity score, textual description of the vulnerability, and the date this vulnerability was discovered (snippet provided in Table1). You also have data about the software products used in the organization containing the software used in the organization, the version of the software, whether the software is used in production environment or not, and the date of the last maintenance to the systems with the software (table2). As this is a big organization so they prioritize the vulnerabilities that they need to fix according to how severe the vulnerability is. You can consider the following categorization:
Critical = vulnerabilities with Vulnerability Severity Score higher or equal to 8.5
High = vulnerabilities with Vulnerability Severity Score higher than 5.6 and lower than 8.5
Medium = vulnerabilities with Vulnerability Severity Score higher than 3.6 and lower or equal to 5.5 Low= vulnerabilities with Vulnerability Severity Score lower or equal to 3.5
The security service provider usually provides quick update about new discovered vulnerabilities but due to the need to test and measure severity they delay providing the “Vulnerability Severity Score” by more than 10 days. During these 10 days the organization do not know how dangerous the vulnerabilities are, so they just randomly start fixing them. You acknowledge that this is not the best
security
practice, and you want to help solve this problem.
1. Provide a brief description of how you are going to approach the problem
2. Explain what are the specific pre-processing steps that you need to perform to increase the value of the data provided.
3. Explain what is the machine learning algorithm you are going to consider? Explain why you chose this Machine learning algorithm and why do you see it fit to solve the problem? Explain if there are any modification to the data you need to perform to be able to fit the model. Be advised, since this is your small project, so you are limited in terms of computation power and resources.
Table1
6.9 to 9.1 2.3.1 to 3.3
Vul ID
Vulnerability Type
Affected Software
Affected Software Version
Vulnerability description
Vulnerability Severity Score
Date Discovered
Microsoft
Textual
description of
vulnerability
Textual
description of
vulnerability
Textual
description of
vulnerability
Textual
description of
vulnerability
1 RCE
2 Buffer overflow
Excel
OpenSSH
10
7.5
2021-3-10 2020-1-9 2021-2-3 2020-4-4 2021-3-28
3 Memory Leak Linux Kernel
4 XSS Apache Tomcat
5 Memory Windows corruption 10
Microsoft Excel 7.1 Ubuntu Linux 6.0 OpenSSH 3.2 Windows 10 1708 Apache Tomcat 7.0
before 7.8 5.0.3
7.0.1 to 8.0.91
1607 to 1903
Textual description of vulnerability
Table2
4.5 9.3
Software
Version
Live (production)
Last Maintenance
Yes 2021-24-04 No 2021-18-03 Yes 2021-10-04 Yes 2021-16-03 No 2021-13-04
Question10: (4 Marks)
Consider a database containing information about movies: genre, director, and decade of release. We also have information about which users have watched each movie. The rating for a user on a movie is either 0 or 1. Here is a summary of the database:
Movie Release decade
A 1970s
B 2010s
C 2000s
D 1990s
E 2010s
Consider user U1 is interested in the time
have some existing recommender system R that
Genre Director
Drama D1 Drama D1 Action D2 Action D2 Drama D3
Total numbers of rating
40
500
300 25 1
period
2000s, the director D2 and the genre Drama. We recommended the movie B to user U1.
The recommender system R could be one or more of the following options:
• User-based collaborative filtering
• Item-based collaborative filtering
• Content-based recommender system
A) Given the above dataset, which one(s) do you think R could be? (If more than one option is possible, you need to state them all.) Explain your answer.
B) If some user U2 wants to watch a movie, under what conditions can our recommender system R recommend U2 a movie? If R recommends a movie, how does it do it? If R cannot recommend a movie, give reasons as to why it can’t. State any additional information R might want from U2 for predicting a movie for this user, if required.