Dell EMC Data Science and BigData Certification Questions and Answers

Question :

In which of the following scenario you should apply the Bay's Theorem


1. The sample space is partitioned into a set of mutually exclusive events { A1, A2, . . . , An }.
2. Within the sample space, there exists an event B, for which P(B) > 0.
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4. In all above cases


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Question :

Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year.
Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts
rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. Which of the following
will you use to calculate the probability whether it will rain on the day of Marie;s wedding?


1. Naive Bayes
2. Logistic Regression
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4. All of the above


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The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following
conditions exist.
" The sample space is partitioned into a set of mutually exclusive events { A1, A2, . . . , An }.
" Within the sample space, there exists an event B, for which P(B) > 0.
" The analytical goal is to compute a conditional probability of the form: P( Ak | B ).






Question : Your company has organized an online campaign for feedback on product quality and you
have all the responses for the product reviews, in the response form people have check box as well
as text field. Now you know that people who do not fill in or write non-dictionary word in the text
field are not considered valid feedback. People who fill in text field with proper English words
are considered valid response. Which of the following method you should not use to identify whether
the response is valid or not?

1. Naive Bayes
2. Logistic Regression
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4. Any one of the above


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In this problem you have been given high-dimensional independent variables like yes, no, no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below
technique can be applied to this problem.

Support vector machines
Naive Bayes
Logistic regression
Random decision forests

Related Questions

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Question : In which of the following scenario we can use naive Bayes theorem for classification

1. Classify whether a given person is a male or a female based on the measured features. The features include height, weight, and foot size.
2. To classify whether an email is spam or not spam
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4. All 1,2 and 3
5. None of the above



Question :

Select the choice where Regression algorithms are not best fit

1. When the dimension of the object given
2. Weight of the person is given
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4. Employee status





Question : Logistic regression does not work well in case of binary classification


1. True
2. False




Question : Which of the following is not the Classification algorithm?


1. Logistic Regression
2. Support Vector Machine
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4. Hidden Markov Models
5. None of the above




Question : Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything
about this conversation, the probability that he was speaking to a woman is 50% (assuming the train had an equal
number of men and women and the speaker was as likely to strike up a conversation with a man as with a woman).
Now suppose he also told you that his conversational partner had long hair. It is now more likely he was speaking
to a woman, since women are more likely to have long hair than men. ____________ can be used to calculate
the probability that the person was a woman.

1. SVM
2. MLE
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4. Logistic Regression




Question : Bayes' theorem cannot finds the actual probability of an event from the results of your tests?


1. True
2. False