Cloudera Databricks Data Science Certification Questions and Answers (Dumps and Practice Questions)

Question : Select the statement which applies correctlty to the Naive Bayes


1. Works with a small amount of data
2. Sensitive to how the input data is prepared
3. Works with nominal values
4. All of above



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Naive Bayes
Pros: Works with a small amount of data, handles multiple classes
Cons: Sensitive to how the input data is prepared
Works with: Nominal values





Question :

Select the correct statement which applies to Bayes rule


1. Bayesian probability and Bayes' rule gives us a way to estimate unknown probabilities from known values.
2. You can reduce the need for a lot of data by assuming conditional independence among the features in your data.
3. Bayes' theorem finds the actual probability of an event from the results of your tests.
4. Only 1 and 2
5. All 1,2 and 3 are correct


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Using probabilities can sometimes be more effective than using hard rules for classification. Bayesian probability and Bayes' rule gives us a way to estimate unknown probabilities from known values.
You can reduce the need for a lot of data by assuming conditional independence among the features in your data. The assumption we make is that the probability of one word doesn't depend on any other words in the document. We know this assumption is a little simple. That's why it's known as naive Bayes. Despite its incorrect assumptions, naive Bayes is effective at classification.
Bayes' theorem finds the actual probability of an event from the results of your tests. For example, you can:
" Correct for measurement errors. If you know the real probabilities and the chance of a false positive and false negative, you can correct for measurement errors.
" Relate the actual probability to the measured test probability. Bayes' theorem lets you relate Pr(A|X), the chance that an event A happened given the indicator X, and Pr(X|A), the chance the indicator X happened given that event A occurred. Given mammogram test results and known error rates, you can predict the actual chance of having cancer.






Question : Which of the following technique can be used to the design of recommender systems?

1. Naive Bayes classifier
2. Power iteration
3. Collaborative filtering
4. 1 and 3
5. 2 and 3

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Explanation: : One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation

Related Questions

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Question : Of all the smokers in a particular district, % prefer brand A and % prefer brand B.
Of those smokers who prefer brand A, 30% are females, and of those who prefer brand B, 40% are female.
What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?

Which of the following is a best way to solve this problem?

1. Bays Theorem
2. Poission Distribution
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4. None of the above




Question : Google Adwords studies the number of men, and women, clicking the advertisement on search engine
during the midnight for an hour each day. Google find that the number of men that click can be modeled as a
random variable with distribution Poisson(X), and likewise the number of women that click as Poisson(Y).

What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour ?



1. Binomial(X+Y,X+Y)
2. Poisson(X/Y)
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4. Poisson(X+Y)


Question :
There are 5000 different color balls, out of which 1200 are pink color.
What is the maximum likelihood estimate for the proportion of "pink" items in the test set of color balls?

1. 2.4
2. 24
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4. .48
5. 4.8



Question :

If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

1. P(E1)/P(E2)
2. P(E1+E2)/P(E1)
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4. P(E2)/(P(E1+E2)



Question :

What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?


1. 1/3
2. 2/3
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4. 2/6


Question : . Let A denote the event `student is female' and let B denote the event 'student is French'.
In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females.
Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).

1. 1/3
2. 2/3
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4. 2/6