Coding Tech Life: April 2017

Data Storage Technologies and Networks - ISZC446 Mid-Semester Examination Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Data Storage Technologies and Networks - ISZC446

Mid-Semester Examination Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Cloud Computing - SSZG527 - Comprehensive Examination Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Cloud Computing - SSZG527

Comprehensive Examination Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Distributed Data Systems - SSZG554 Comprehensive Examination Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Distributed Data Systems - SSZG554

Comprehensive Examination Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Data Storage Technologies and Networks - ISZC446 Comprehensive Examination Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Data Storage Technologies and Networks - ISZC446

Comprehensive Examination Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Data Mining - ISZC415 - Comprehensive Examination Question Paper - 2017 Makeup BITS PILANI WILP MTec Software Systems

Data Mining - ISZC415

Comprehensive Examination Question Paper - 2017 Makeup

BITS PILANI WILP MTec Software Systems

Artificial Intelligence - ISZC444 Comprehensive Question Paper - 2017 Make up BITS PILANI WILP MTec Software Systems

Artificial Intelligence - ISZC444

Comprehensive Question Paper - 2017 Make up

BITS PILANI WILP MTec Software Systems

Data Warehousing - SSZG515 Comprehensive Question Paper - 2017 Make up BITS PILANI WILP MTec Software Systems

Data Warehousing - SSZG515

Comprehensive Question Paper - 2017 Make up

BITS PILANI WILP MTec Software Systems

Usability Engineering -SSZG547 - Comprehensive Question Paper - 2017 Make up BITS PILANI WILP MTec Software Systems

Usability Engineering -SSZG547

Comprehensive Question Paper - 2017 Make up

BITS PILANI WILP MTec Software Systems

Usability Engineering - Assignment - Wireframe/mockup using Balsamiq

Faculty APP - the application is to find a substitute in their place when they are not available on a given day.

About Tool

Balsamiq is an application for building mockup or wireframes for web, ios android and other kinds of applications. It is easy to create wireframes using this tool since it has got drag and drop features. of widgets.

Wirefraemes:

1. Specifications for home Screen

Registered User: Faculty is already registered to app. He can log in using username and password.
New User: He needs to click on the New user/Signup.
Forgot Password option to recover the password should be there.

Assumptions: The user/faculty is a teaching faculty in the XYZ college. User is having a phone number and email address.

1. Existing user: logs in the app using registered phone number/email address and password.

If user forgot his password, click on 'forgot password' link.

2. New User: Clicks on Register to create a new user.

2. Registered User After logging In:

Faculty has logged in.

Specifications:

1. Faculty details along with his timetable should be shown.

2. Faculty should be able to select a particular day, so that next he should be able to see which faculties are free in those slots whom he can substitute.

3. Logout option should be there on all screens displayed after logging in.

Assumptions:

Faculty logged in.

2.1 User Details

User clicks on Profile to see his/her details. User details can be edited by clicking edit button. User can go back to home page by clicking back button.

User clicks on Schedule and select a date to view the time table for particular date.

System assumes schedule of the faculty is generated by system or admin

2.2 Substitution

User clicks on substitution, Select a date and select slots he want substitution. Then click on available faculties, system will list suitable and available faculties. Substitution faculty can be selected from the list using radio button. Details of the faculties can be viewed by clicking on View full profile. After selecting faculty, click on submit. Successful message will be shown on next screen. On click of ok button it will redirect to home page.

2.3 Notifications

The substitution faculty will be notified by text message, email and notification in the app. The request can be accepted or rejected by the faculty.

2.4 Logout

Faculty can log out at any point by clicking log out in side menu.

Data Mining IS ZC415 Comprehensive Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Data Mining IS ZC415

Comprehensive Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Information Retrieval SSZG537 Comprehensive Question Paper - 2017 Regular BITS PILANI WILP MTec Software Systems

Information Retrieval SSZG537

Comprehensive Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Usability Engineering -SSZG547 Comprehensive Question Paper - 2016-2017 Regular BITS PILANI WILP

Usability Engineering -SSZG547

Comprehensive Question Paper - 2017 Regular

BITS PILANI WILP MTec Software Systems

Data Warehousing Quiz 3 BITS WILP - Mtec Software Systems

Data Warehousing Quiz 3

BITS WILP - Mtec Software Systems

1. Theoretically, what kind of views we can materialize?
Select one:
a. Any kind of view can be materialized
b. Only involving joins & aggregates
c. Only involving joins
d. Only involving aggregation

Ans: a. Any kind of view can be materialized

2. For partitioning wrt time dimension, which kind of partitioning method is most suitable
Select one:
a. Hash partitioning
b. Composite partitioning
c. Range partitioning
d. List partitioning

Ans: c. Range partitioning

3. User queries and application programs need not be aware of
Select one:
a. Existing partitions only
b. Existing partitions, aggregates, and materialised views
c. Existing aggregates only
d. Existing materialized views only

Ans: b. Existing partitions, aggregates, and materialised views

4. Online aggregation:
Select one:
a. Improves query performance
b. Uses blocking algorithms for evaluating relational operators
c. Does not allow users to prioritise
d. Provides early trends

Ans: d. Provides early trends

5. Size of the bitmap index on a column of a relation R increases with
Select one:
a. An increase in number of attributes of R
b. An increase in column cardinality
c. An increase in the width of the column
d. An increase in number of queries on R

Ans: b. An increase in column cardinality

6. The most generalized term:
Select one:
a. Precomputed joins
b. Precomputed joins with aggregates
c. Precomputed aggregates
d. Materialized views

Ans: c. Precomputed aggregates

7. A query performance enhancing technique that has the least space
overheads:
Select one:
a. View materialization
b. Aggregations
c. Bitmap indices
d. Partitioning

Ans: d. Partitioning

8. Bitmap indexes are:
Select one:
a. Multidimensional indexes
b. Dynamic indexes
c. Multilevel indexes
d. Dense indexes

Ans: a. Multidimensional indexes

9. The aggregate navigation algorithm orders the base and aggregated
fact tables from
Select one:
a. Smallest to the biggest in terms of space requirement
b. Most frequently used to least frequently used
c. Smallest to the biggest in terms of number of tuples
d. 3-way to 2-way to 1-way to base level

Ans: c. Smallest to the biggest in terms of number of tuples

10. Aggregate navigator is a:
Select one:
a. Materialized view generator
b. Middleware
c. End-user tool
d. View maintenance software

Ans: b. Middleware

11. Partitioning wrt time dimension is recommended because:
Select one:
a. It is easier to do as compared to partitioning wrt other dimensions
b. it can be done using range partitioning
c. It facilitates incremental view maintenance
d. It facilitates incremental back up

Ans: c. It facilitates incremental view maintenance
d. It facilitates incremental back up

12. For partitioning wrt product dimension, which kind of partitioning
method is most suitable
Select one:
a. Hash partitioning
b. Composite partitioning
c. Range partitioning
d. List partitioning

Ans: d. List partitioning

13. Which kind of partitioning would create almost equal size partitions:
Select one:
a. Hash partitioning
b. Composite partitioning
c. Range partitioning
d. List partitioning

Ans: a. Hash partitioning

14. Materialized views:
Select one:
a. Store redundant data
b. Always give current data like views
c. Do not need maintenance like views
d. Do not incur space overheads

Ans: a. Store redundant data

15. From the ETL point of view, it is simplest to handle:
Select one:
a. Highly aggregated data
b. Finest granularity data

c. Lightly aggregated data
d. Medium granularity data

Ans: b. Finest granularity data

Data Warehousing SSZG515 Comprehensive Examination Question Paper 2017

Data Warehousing SS ZG515

Comprehensive Examination Question Paper 2017

BITS WILP MTec Software Systems

Artificial Intelligence IS ZC444 Comprehensive Examination Question Paper 2017 BITS WILP MTec Software Systems

Artificial Intelligence IS ZC444

Comprehensive Examination Question Paper 2017

BITS WILP MTec Software Systems

Machine Learning (ISZC464) Comprehensive Question Paper - 2016-2017 Regular BITS PILANI WILP

Machine Learning (ISZC464)

Comprehensive Question Paper - 2016-2017 Regular

BITS PILANI WILP

Usability Engineering SSZG547 Quiz 2 MTec Software Systems - BITS PILANI

Usability Engineering SSZG547 Quiz 2

MTec Software Systems - BITS PILANI

1. Qualitative research helps to understand

Select one or more:
a. Behaviors
b. Faults
c. Domain of products
d. Attitudes

Ans: Behaviors, Attitudes, Domain of products

2. Usability testing helps in determining

Select one or more:
a. Organization
b. How easy to discover and use for the first time
c. Naming
d. How effective is the design

Ans: Naming, Organization, How easy to discover and use for the first time, How effective is the design

3.Benefits of a grid system in visual interface design

Select one or more:
a. Efficiency
b. Usability
c. Fitness
d. Aesthetic appeal
e. None of the answers

Ans: Usability, Aesthetic appeal, Efficiency

4. Basic Visual Usability principles:

Select one or more:
a. Consistency
b. None of the answers
c. Personality
d. Hierarchy

Ans: Consistency, Hierarchy, Personality

5. Ethnographic interview methods:

Select one or more:
a. Encourage story telling
b. First focusing on goals
c. avoiding technology related discussions
d. Make the user as a designer

Ans: First focusing on goals, Encourage story telling, avoiding technology related discussions

6. Market Surveys:

Select one:
a. Both qualitative and quantitative research
b. None of the answers
c. Qualitative Research
d. Quantitative Research

Ans: Quantitative Research

7. Qualitative survey:

Select one or more:
a. Market Survey
b. Subjective knowledge
c. Behavioral knowledge
d. Objective questionnaire
e. Web Poll

Ans: Behavioral knowledge, Subjective knowledge

8. Hierarchy helps in:

Select one or more:
a. to bring the focus
b. Presentation
c. None of the answers
d. structuring

Ans: Presentation, structuring, to bring the focus

9. Personas:

Select one:
a. same as prototypes
b. same as stereotypes
c. None of the answers
d. provides a precise design target

Ans: provides a precise design target

10. The way of placing some elements within other in design user interfaces follows the principle of

Select one:
a. Mixing
b. Nesting
c. Overlapping
d. None of the answers
e. Treatment

Ans: Nesting

11. Select Mechanical age representations

Select one or more:
a. Folder containing papers
b. Paper calendar
c. Google calendar
d. Physical address book
e. None of the answers

Ans: Paper calendar, Folder containing papers, Physical address book

12. Persona set is essential to capture

Select one:
a. Multiple User behavior
b. Common user behavior
c. Ranges of user behavior
d. User behavior statistics

Ans: Ranges of user behavior

13. Prototypes are:

Select one:
a. same as wire frames
b. less expensive as compared to wire frames.
c. None of the answers
d. Expensive as compared to wire frames

Ans: Expensive as compared to wire frames

14. Archetypes are

Select one or more:
a. Based on motivations
b. Stereotypes
c. Based on Generalization
d. Based on behavior pattern

Ans: Based on behavior pattern, Based on motivations

15. Unstructured interviews are a good way of building personas

Select one:
a. False
b. True

Ans: False

16. More than three or four secondary personas is a sign of

Select one:
a. None of the answers
b. No scope at all
c. product scope is too small and unfocused
d. product scope is too large and unfocused

Ans: product scope is too large and unfocused

17. Personas are

Select one:
a. Zombies
b. None of the answers
c. A single person
d. Imaginary people

Ans: None of the answers

18. Usability testing:

Select one:
a. measuring how well a user can complete a given task
b. same as structural testing
c. is same as black box testing
d. same as white box testing
e. None of the answers

Ans: measuring how well a user can complete a given task

19. Personas are sometimes referred as

Select one:
a. Composite archetypes
b. Typical stereotypes
c. Composite stereotypes
d. Typical archetypes

Ans: Composite archetypes

20. Self Referential design is

Select one:
a. Developer centric
b. None of the answers
c. User centric
d. Tester centric

Ans: Developer centric

21. Identify the methods that could be used to navigate information

Select one or more:
a. Scrolling
b. Zooming
c. Linking
d. Panning

Ans: Scrolling, Linking, Zooming, Panning

22. Building blocks of visual interface design

Select one or more:
a. dullness
b. fitness
c. None of the answers
d. Orientation
e. texture

Ans: Orientation, texture

23. Secondary personas can also be

Select one:
a. Primary personas
b. Negative personas
c. Served personas
d. Supplemental personas

Ans: Served personas

24. Data collected through qualitative research:

Select one or more:
a. None of the answers
b. Descriptive data
c. Not in numerical format
d. Difficult to analyze

Ans: Not in numerical format, Difficult to analyze, Descriptive data

25. The best way to get User data/behavior

Select one or more:
a. Guessing
b. Interviews
c. None of the options
d. Observation

Ans : Observation, Interviews

26. Select the visual properties that is of significance with respect to the below picture.

Select one or more:
a. Position
b. Shape
c. Hue
d. Orientation
e. Size
f. Value

Ans: Hue, Orientation

27. stakeholder interviews:

Select one or more:
a. None of the answers
b. better to do it in isolation
c. should be done before user research begins
d. done with all the stakeholders together.

Ans: better to do it in isolation, should be done before user research begins

28. Customers:

Select one or more:
a. are not consumers
b. are always consumers
c. may be consumers
d. may not be consumers

Ans: may be consumers, may not be consumers

29. Customer Journey maps are used:

Select one:
a. to create site maps
b. to understand the weather
c. to understand the user
d. None of the answers

Ans: to understand the user

30. SME's are

Select one or more:
a. None of the options
b. Designers
c. Not Designers
d. Domain Experts

Ans: Not Designers, Domain Experts

Machine Learning - ZC464 - Quiz 2 BITS PILANI WILP - 2017

Machine Learning (ISZC464) Quiz 2

BITS PILANI WILP - 2017

1. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many possible different examples are there?
Select one:
a. 12
b. 48
c. 24
d. 72

Ans: d. 72

2. Which of the following statements are true for k-NN classifiers
Select one:
a. The decision boundary is linear.
b. The decision boundary is smoother with smaller values of k.
c. k-NN does not require an explicit training step.
d. The classification accuracy is better with larger values of k.

Ans: c. k-NN does not require an explicit training step.

3. Which of the following statements are false?
Select one:
a. Decision tree is learned by maximizing information gain
b. Density estimation (using say, the kernel density estimator) can be used to perform classification.
c. No classifier can do better than a naive Bayes classifier if the distribution of the data is known
d. The training error (error on training set) of 1-NN classifier is 0

Ans: c. No classifier can do better than a naive Bayes classifier if the distribution of the data is known

4. Suppose we wish to calculate P(H | E1, E2) and we have no conditional independence information. Which of the following sets are sufficient for computing this (minimal set)?
Select one:
a. P(E1, E2| H) , P(H), P(E1|H), P(E2|H)
b. P(E1, E2), P(H), P(E1, E2| H)
c. P(E1, E2) , P(H), P(E1|H), P(E2|H)
d. P(H), P(E1| H), P(E2|H)
Feedback

Ans: b. P(E1, E2), P(H), P(E1, E2| H)

5. In neural networks, nonlinear activation functions such as sigmoid and tanh
Select one:
a. help to learn nonlinear decision boundaries
b. always output values between 0 and 1
c. speed up the gradient calculation in backpropagation, as compared to linear units
d. are applied only to the output units
Feedback

Ans: a. help to learn nonlinear decision boundaries

6. Which of the following statements about Naive Bayes is incorrect?
Select one:
a. Attributes are statistically independent of one another given the class value.
b. Attributes are statistically dependent of one another given the class value.
c. Attributes can be nominal or numeric
d. Attributes are equally important.

Ans: b. Attributes are statistically dependent of one another given the class value.

7. As the number of training examples goes to infinity, your model trained on that data will have:
Select one:
a. Lower variance
b. None of the other options
c. Higher Variance
d. Does not affect variance
Feedback

Ans: a. Lower variance

8. Which of the following statements are true?
Select one:
a. The depth of a learned decision tree can be larger than the number of training examples used to create the tree.
b. Suppose data has R records, the maximum depth of the decision tree must be less than 1 + log2R
c. Cross validation can be used detect and reduce overfitting
d. As the number of data points grows to infinity, the MAP estimate approaches the MLE estimate for all possible priors. In other words, given enough data, the choice of prior is irrelevant.

Ans: c. Cross validation can be used detect and reduce overfitting

9. Which of the following strategies cannot help reduce overfitting in decision trees?
Select one:
a. Make sure each leaf node is one pure class
b. Enforce a maximum depth for the tree
c. Enforce a minimum number of samples in leaf nodes
d. Pruning
Feedback

Ans: a. Make sure each leaf node is one pure class

10. If A and B are conditionally independent given C, are A and B independent, which of the following is not true?
Select one:
a. P(B|A, C) = P(B|C)
b. P(A,B| C) = P(A) P(B)
c. P(A,B,C) = P(C) P(A|C) P(B|C)
d. P(A|B, C) = P(A|C)
Feedback

Ans: b. P(A,B| C) = P(A) P(B)

11. Which of the following statements are false?
Select one:
a. We can get multiple local optimum solutions if we solve a linear regression problem by minimizing the sum of squared errors using gradient descent.
b. When a decision tree is grown to full depth, it is more likely to fit the noise in the data
c. When the hypothesis space is richer, over fitting is more likely
d. We can use gradient descent to learn a Gaussian Mixture Model.

Ans: a. We can get multiple local optimum solutions if we solve a linear regression problem by minimizing the sum of squared errors using gradient descent.

12. Suppose we wish to calculate P(H | E1, E2) and we know that P(E1| H, E2) = P(E1|H) for all the values of H, E1, E2. Now which of the following sets are sufficient?
Select one:
a. P(E1, E2) , P(H), P(E1|H), P(E2|H)
b. P(E1, E2), P(H), P(E1, E2| H)
c. P(H), P(E1| H), P(E2|H)
d. P(E1, E2| H) , P(H), P(E1|H), P(E2|H)

Ans: b. P(E1, E2), P(H), P(E1, E2| H)

13. As the number of training examples goes to infinity, your model trained on that data will have:
Select one:
a. Lower Bias
b. Same Bias
c. Higher Bias
d. None of the other options

Ans: b. Same Bias

14. For polynomial regression, which one of these structural assumptions is the one that most affects the trade-off between underfitting and overfitting
Select one:
a. The assumed variance of the Gaussian noise
b. Whether we learn the weights by gradient descent
c. The use of a constant-term unit input
d. The polynomial degree

Ans: d. The polynomial degree

15. High entropy means that the partitions in decision tree classification are
Select one:
a. Not pure
b. Pure
c. Useful
d. Useless

Ans: a. Not pure