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iv.   Assertion (A): Stories that combine statistics and analytics are more persuasive.
               Reason (R): When we talk about data storytelling, we're talking about stories in which data plays a central role.
               Select the appropriate option for the statements given above:                                    1
               (a) Both A and R are true and R is the correct explanation of A

               (b) Both A and R are true and R is not the correct explanation of A
               (c) A is true but R is false

               (d) A is False but R is true
            v.  Which of the following is not a feature of RMSE?                                                1
               (a) It tells about the accuracy of the model.

               (b) Higher value means hyper parameters need to be tweaked.
               (c) Lower RMSE values are not good for the AI model.
               (d) RMSE is a measure of how evenly distributed residual errors are.
            (vi) Once you have got an AI model that's ready for production, AI engineers then ………………………. a trained model,
               making it available for external inference requests.                                             1
               (a) Evaluate                                  (b) Test
               (c) Deploy                                    (d) Redesign

        4.  Answer any 5 out of the given 6 questions.                                       (1 × 5 = 5 marks)
             i.  Data Validation for human biases is conducted in ………………………. phase of AI Model Life Cycle.      1
               (a) Scoping                                   (b) Data Collection
               (c) Design                                    (d) Testing
            ii.  Identify the following icons:                                                                  1

               (a)                                           (b)


            iii.  Which of the following is a disadvantage of Cross Validation Technique?                       1
               (a) Cross-validation provides insight into how the model will generalise to a new dataset.

               (b) Cross-validation aids in determining a more accurate model prediction performance estimate.
               (c) As we need to train on many training sets, cross-validation is computationally expensive.
               (d) Cross-validation could result in more precise models.
            iv.  Hyper parameters are parameters whose values govern the learning process.                      1
               (a) True                                      (b) False
            v.  The steps that assist in finding compelling stories in the data sets are as follows. Arrange them in proper order:   1
               1.  Visualize the data.
               2.  Examine data relationships.
               3.  Get the data and organise it.
               4.  Create a simple narrative embedded with conflict.
               (a) 1-2-3-4                                   (b) 2-3-1-4
               (c) 4-1-3-2                                   (d) 3-1-2-4
            vi.  Choose the difference between Regression and Classification Loss functions from the following:   1
               (a) Regression functions predict a quantity, and classification functions predict a label.
               (b) Regression functions predict a label, and classification functions predict a quantity.
               (c) Regression functions predict a qualitative value, and classification functions predict a label.

               (d) Regression functions predict a label, and classification functions predict a qualitative value.




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