Data Analytics mcq with answers unit 4

Data Analytics mcq with answers

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Data Analytics mcq with answers | big data analytics mcq

1. A ____ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

  1. Decision tree
  2. Graphs
  3. Trees
  4. Neural Networks
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Decision tree

2. What is Decision Tree?

  1. Flow-Chart
  2. Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
  3. Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
  4. None of Above
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Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

3. Decision Trees can be used for Classification Tasks.

  1. TRUE
  2. FALSE
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TRUE

4. Choose from the following that are Decision Tree nodes?

  1. Decision Nodes
  2. End Nodes
  3. Chance Nodes
  4. All of Above
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All of Above

5. Decision Nodes are represented by __

  1. Disks
  2. Squares
  3. Circles
  4. Triangles
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Squares

6. Chance Nodes are represented by __

  1. Disks
  2. Squares
  3. Circles
  4. Triangles
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Circles

7.  End Nodes are represented by __

  1. Disks
  2. Squares
  3. Circles
  4. Triangles
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Triangles

8. Which of the following are the advantage/s of Decision Trees?

  1. Possible Scenarios can be added
  2. Use a white box model, If given result is provided by a model
  3. Worst, best and expected values can be determined for different scenarios
  4. All of Above

All of Above

9. Which of the following statements about Naive Bayes is incorrect?

  1. Attributes are equally important.  
  2. Attributes are statistically dependent of one another given the class value.
  3. Attributes are statistically independent of one another given the class value.  
  4. Attributes can be nominal or numeric
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Attributes are statistically dependent of one another given the class value.

10. Which of the following is not supervised learning?

  1. Clustering
  2. Decision Tree
  3. Linear Regression
  4. Naive Bayesian
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Clustering

Data analytics mcq with answers

11. How many terms are required for building a bayes model?

  1. 1
  2. 2
  3. 3
  4. 4

3

12. Where does the bayes rule can be used?

  1. Solving queries
  2. Increasing complexity
  3. Decreasing complexity
  4. Answering probabilistic query
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Answering probabilistic query

13. How the bayesian network can be used to answer any query?

  1. Full distribution
  2. Joint distribution
  3. Partial distribution
  4. All of Above

Joint distribution

14. What is the consequence between a node and its predecessors while creating bayesian network?

  1. Functionally dependent
  2. Dependant
  3. Conditionally independent
  4. Both Conditionally dependant & Dependant
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Conditionally independent

15. Bayesian classifiers is

  1. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
  2. Any mechanism employed by a learning system to constrain the search space of a hypothesis
  3. An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
  4. None of these

A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.

16. Bias is

  1. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory
  2. Any mechanism employed by a learning system to constrain the search space of a hypothesis
  3. An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
  4. None of these
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Any mechanism employed by a learning system to constrain the search space of a hypothesis

17. Background knowledge referred to

  1. Additional acquaintance used by a learning algorithm to facilitate the learning process
  2. A neural network that makes use of a hidden layer
  3. It is a form of automatic learning.
  4. None of these

Additional acquaintance used by a learning algorithm to facilitate the learning process

18. Discriminating between spam and ham e-mails is a classification task

  1. TRUE
  2. FALSE
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TRUE

19. which of the following is not involve in data mining?

  1. Knowledge extraction
  2. Data archaeology
  3. Data exploration
  4. Data transformation

Data transformation

20. Naive prediction is

  1. A class of learning algorithms that try to derive a Prolog program from examples
  2. A table with n independent attributes can be seen as an n- dimensional space.
  3. A prediction made using an extremely simple method, such as always predicting the same output.
  4. None of these

A prediction made using an extremely simple method, such as always predicting the same output.

data analytics mcq questions and answers

21. Node is ____

  1. A component of a network
  2. In the context of KDD and data mining, this refers to random errors in a database table.
  3. One of the defining aspects of a data warehouse
  4. None of these
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A component of a network

22. Prediction is

  1. The result of the application of a theory or a rule in a specific case
  2. One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table.
  3. Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces.
  4. None of these

The result of the application of a theory or a rule in a specific case

23. What is the relation between the distance between clusters and the corresponding class discriminability?

  1. proportional
  2. inversely-proportional
  3. no-relation
  4. None of these
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proportional

24. the classification method in which the upper limit of interval is same as of lower class interval is called

  1. exclusive method
  2. inclusive method
  3. mid point method
  4. None of these

exclusive method

25. larger value is 60 and the smallest value is 40 and the number of classes is 5 then the class interval is

  1. 20
  2. 25
  3. 4
  4. 15

4

Big data analytics mcq

26. summary and presentation of data in tabular form with several non overlapping classes is referred as

  1. nominal distribution
  2. frequency distribution
  3. ordinal distribution
  4. None of these
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frequency distribution

27. the classification method in which the upper and lower limit of interval is also in class interval itself is called

  1. exclusive method
  2. inclusive method
  3. mid point method
  4. None of these

inclusive method

28. Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble of above 25 classifiers will make a wrong prediction? Note: all classifiers are independent of each other

  1. 0.05
  2. 0.06
  3. 0.07
  4. 0.08
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0.06

29. The most widely used metrics and tools to assess a classification model are:

  1. Confusion matrix
  2. Cost-sensitive accuracy
  3. Area under the ROC curve
  4. All of Above

All of Above

30. When performing regression or classification, which of the following is the correct way to preprocess the data?

  1. Normalize the data → PCA → training
  2. PCA → normalize PCA output → training
  3. Normalize the data → PCA → normalize PCA output → training
  4. None of these
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Normalize the data → PCA → training

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31. Which of the following is true about Naive Bayes ?

  1. Assumes that all the features in a dataset are equally important
  2. Assumes that all the features in a dataset are independent
  3. both a and b
  4. None of these

both a and b

32. In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes

  1. 1 and 2
  2. 2 and 3
  3. 1, 2, and 3
  4. 1 and 3
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1, 2, and 3

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