Dell EMC Data Science and BigData Certification Questions and Answers

Question :

Digit recognition, is an example of___________


1. Classification
2. Clustering
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4. None of the above



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Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition, once again, is a common example
of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not
even be necessary to give pre determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification
context.







Question : Clustering is a type of unsupervised learning with the following goals



1. Maximize a utility function
2. Find similarities in the training data
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4. 1 and 2
5. 2 and 3


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Explanation: : type of unsupervised learning is called clustering. In this type of learning,
The goal is not to maximize a utility function, but simply to find similarities in the training data.
The assumption is often that the clusters discovered will match reasonably well with an intuitive classification.
For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another.
Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further
results due to dependencies in the data.









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



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Explanation:

Related Questions

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Question :

Which of the following is an correct example of the target variable in regression (supervised learning) ?

1. Nominal values like true, false
2. Reptile, fish, mammal, amphibian, plant, fungi
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4. Only 1 and 2
5. All 1,2 and 3




Question : Select the sequence of the developing machine learning applications
A. Analyze the input data
B. Prepare the input data
C. Collect data
D. Train the algorithm
E. Test the algorithm
F. Use It



1. A,B,C,D,E,F
2. C,B,A,D,E,F
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4. C,B,A,D,E,F


Question :

Select the correct statement which applies to K-Nearest Neighbors

1. No Assumption about the data
2. Computationaly expensive
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4. Works with Numeric Values


1. 1,2,3,4
2. 2,3,4
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4. 1,2,4



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
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4. All of above




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.
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4. Only 1 and 2
5. All 1,2 and 3 are correct



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

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