Databricks Certified Professional Data Scientist - Databricks-Certified-Professional-Data-Scientist FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

Which of the below best describe the Principal component analysis

A. Dimensionality reduction B. Regression C. Classification D. Collaborative filtering E. Clustering

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Correct Answer: A Vote an answer

Question 2

Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...

A. L1 is the sum of the square of the weights, while L2 is just the sum of the weights B. None of the above C. L2 is the sum of the square of the weights, while L1 is just the sum of the weights D. L1 gives Non-sparse output while L2 gives sparse outputs

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Correct Answer: C Vote an answer

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Question 3

You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?

A. Association rules B. Linear regression C. K-means clustering D. Decision trees

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Correct Answer: C Vote an answer

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Question 4

You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc.
During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?

A. Discovery B. Model planning C. Model building D. Data Preparation E. Operationalize

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Correct Answer: C Vote an answer

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Question 5

In which of the scenario you can use the linear regression model?

A. Predicting tumor size reduction based on input as number of radiation treatment B. Predicting Home Price based on the location and house area C. Predicting demand of the goods and services based on the weather D. Predicting sales of the text book based on the number of students in state

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Correct Answer: A,B,C,D Vote an answer

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Question 6

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?

A. Random Decision Forests B. Any one of the above C. Naive Bayes D. Logistic Regression

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Correct Answer: B Vote an answer

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Question 7

Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:
In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.
Select the correct statement

A. 2 and 3 B. Precision is low, which means the classifier is predicting positives poorly C. 1 and 3 D. Precision is low, which means the classifier is predicting positives best E. problem domain has a major impact on the measures that should be used to evaluate a classifier within it

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Correct Answer: A Vote an answer

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Question 8

A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

A. Presence or absence of the other features B. None of the above C. Absence of the other features. D. Presence of the other features.

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Correct Answer: A Vote an answer

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Question 9

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

A. Maximum likelihood estimation B. Linear Regression C. Recommendation system D. Hierarchical linear models E. Logistic Regression

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Correct Answer: E Vote an answer

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Question 10

Which of the following could be features?

A. Characteristics of an unidentified object B. Symptoms of a diseases C. All 1,2 and 3 are possible D. 0nly 1 and 2 E. Words in the document

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Correct Answer: C Vote an answer

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