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iii.     Assertion (A):  o adays, predictive models are not able to better predict rare events such as disease or system
                    failure.
                     Reason (R):  oday's high performance database analytics enable data scientists to utilise large datasets that
                    contain large or even all of the available data.
             Ans.   d
               iv.    Assertion (A):  e follo  problem decomposition methodology that can be applied to real life problems as  ell.
                   Reason (R):  eal life problem solving is complicated.
             Ans.   a
                v.     Assertion (A):  he data scientist revie s the model during development and before deployment to determine
                    its quality and ensures that it correctly and completely ans ers the business problem.
                   Reason (R): Data scientist uses statistical significance tests to verify the model's accuracy.
             Ans.   b

               vi.     Assertion (A): After the initial data collection, techniques such as descriptive statistics and visualisations are
                    applied to datasets.
                   Reason (R):  his step is carried out to evaluate the content, quality, and initial insights of the data.
             Ans.   a
               vii.     Assertion (A):  oss function learns to increase prediction error over time  ith the help of some  optimisation
                    ob ective function.
                   Reason (R): A loss function is used by machines to learn.
             Ans.   d
              viii.    Assertion (A):  eal  orld data can be strange and deceptive.
                   Reason (R): Data can be gathered from a variety of sources, including  overnment  ebsites.
             Ans.   b
               i .     Assertion (A):  he  oot  ean  quare  rror      ) is a metric for determining ho   ell a regression line fits the
                    data points.
                   Reason (R):      places a higher  eight on the errors due to the squaring element of the function.
             Ans.   c
                 .   Assertion (A): A split technique divides the provided dataset into t o subsets   training and test subset.
                    Reason (R): As a result, the process is frequently referred to as k fold cross validation.
             Ans.   c
              .   hich of these is  O  analytic based on the type of question                    ample  aper,
               a.   Descriptive                                  b.   tatistical Analysis
               c.    orecasting                                  d.  Data evaluation
           Ans. d

              .   hich of the follo ing statements is are I  O                                   ample  aper,
               i)   Different transforms of the data used to train the same machine learning model.
               ii)   Different machine learning models cannot be trained on the same data.
               iii)  Different configurations for a machine learning model trained on the same data
               a.   i)                                           b.  ii)
               c.    oth ii)   iii)                              d.   oth i)   ii)
           Ans. b
              .   If the problem is based on probabilities of an action, then  hich analytic approach
               can be used                                                                       ample  aper,
               a.    redictive  odel                             b.   rescriptive
               c.   Diagnostic                                   d.  Descriptive
           Ans. a

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