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

Question : Suppose that we are interested in the factors that influence whether a political candidate wins an election.
The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of
money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

Above is an example of



1. Linear Regression
2. Logistic Regression
3. Recommendation system
4. Maximum likelihood estimation
5. Hierarchical linear models


Correct Answer : 2






Question : A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),
GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school.
The response variable, admit/don't admit, is a binary variable.

Above is an example of



1. Linear Regression
2. Logistic Regression
3. Recommendation system
4. Maximum likelihood estimation
5. Hierarchical linear models


Correct Answer : 2





Question :

In unsupervised learning which statements correctly applies


1. It does not have a target variable
2. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X
3. telling the machine Predict Y for our data X
4. 1 and 3
5. 1 and 2


Correct Answer : 5

Exp In unsupervised learning we don't have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?

Related Questions

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Question : Select the correct characteristics of unsupervised learning


1. Unsupervised learning is that of trying to find hidden structure in unlabeled data
2. There is no error or reward signal to evaluate a potential solution
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4. Only 1 and 2
5. All 1,2 and 3



Question : Principal component analysis (PCA) is an example of

1. Supervised learning
2. Unsupervised Learning
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4. Hidden Markov Models


Question : Unsupervised learning can be used for bridging the causal gap between input and output observations

1. True
2. False


Question : Select the correct statement which applies to neural networks

1. The structure of neural networks is usually represented graphically by showing the computational elements, neurons, of the network
2. Each node corresponds to one neuron and the arrows usually denote weighted sums of the values from other neurons
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On the first layer there are the factors and the second layer consists of linear neurons which compute a weighted sum of their inputs.
4. 1 and 2 only
5. All 1,2 and 3


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



Question : MLE pay attention to P(result|condition), while MAP pay attention to P(condition|result).

1. True
2. False