Dell EMC Data Science and BigData Certification Questions and Answers

Question : You are working on a problem where you have to predict whether the claim is done valid or not.
And you find that most of the claims which are having spelling errors as well as corrections in the manually
filled claim forms compare to the honest claims. Which of the following technique is suitable to find
out whether the claim is valid or not?

1. Naive Bayes
2. Logistic Regression
3. Access Mostly Uused Products by 50000+ Subscribers
4. Any one of the above



Correct Answer : Get Lastest Questions and Answer :

In this problem you have been given high-dimensional independent variables like texts, corrections, 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






Question : . Bayes' Theorem allows you to look at an event that has already happened and make an
educated guess about the chain of events that may have led up to that event

1. True
2. False


Correct Answer : Get Lastest Questions and Answer :







Question :

Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation,
car, bus, or commuter train. Because of high traffic, if he decides to go by car, there is a 50%
chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded,
the probability of being late is only 20%. The commuter train is almost never late, with a probability of
only 1%, but is more expensive than the bus.

Question : Suppose that Bob is late one day, and his boss wishes to estimate the probability that he
drove to work that day by car. Since he does not know which mode of transportation Bob usually uses,
he gives a prior probability of 1 3 to each of the three possibilities. Which of the following method the
boss will use to estimate of the probability that Bob drove to work?


1. Naive Bayes
2. Linear regression
3. Access Mostly Uused Products by 50000+ Subscribers
4. None of the above



Correct Answer : Get Lastest Questions and Answer :


Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.

Related Questions

---

Question : Refer to the exhibit.
Click on the calculator icon in the upper left corner. An analyst is searching a corpus of documents
for the topic "solid state disk". In the Exhibit, Table A provides the inverse document frequency for
each term across the corpus. Table B provides each term's frequency in four documents selected
from corpus. Which of the four documents is most relevant to the analyst's search?

1. A
2. B
3. Access Mostly Uused Products by 50000+ Subscribers
4. D




Question : Refer to the exhibit
Click on the calculator icon in the upper left corner. You are going into a meeting where you know
your manager will have a question on your dataset -- specifically relating to customers that are
classified as renters with good credit status.
In order to prepare for the meeting, you create a rule: RENTER => GOOD CREDIT. What is the
confidence of the rule?

1. 63%
2. 41%
3. Access Mostly Uused Products by 50000+ Subscribers
4. 73%




Question :

One can work with the naive Bayes model without accepting Bayesian probability


1. True
2. False




Question :

Logistic regression is a model used for prediction of the probability of occurrence of an event.
It makes use of several variables that may be___________

1. Numerical
2. Categorical
3. Access Mostly Uused Products by 50000+ Subscribers
4. None of the 1 and 2 are correct




Question : Select the correct statement regarding the naive Bayes classification

1. it only requires a small amount of training data to estimate the parameters
2. Independent variables can be assumed
3. Access Mostly Uused Products by 50000+ Subscribers
4. for each class entire covariance matrix need to be determined


1. 1,2,3
2. 2,3,4
3. Access Mostly Uused Products by 50000+ Subscribers
4. 2,3,4



Question : Spam filtering of the emails is an example of

1. Supervised learning
2. Unsupervised learning
3. Access Mostly Uused Products by 50000+ Subscribers
4. 1 and 3 are correct
5. 2 and 3 are correct