Databricks Certified Professional Data Scientist Exam Dumps

1. Select the correct statement which applies to K-Nearest Neighbors

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...

3. Question-26. 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?

4. Select the correct option which applies to L2 regularization

5. What are the key outcomes of the successful analytical projects?

6. Refer to exhibit





You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1. Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C. The results of the regression are seen in the exhibit. You cannot request additional data.

What is a way that you could try to increase the R2 of the model without artificially inflating it?

7. Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct

8. You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero.

So which of the following algorithm can help you to avoid zero probability?

9. You recommend a movie with three stars but the user loves it (he'd rate it five stars).

So which statement correctly applies?

10. Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.

11. What describes a true property of Logistic Regression method?

12. Which of the following is not a correct application for the Classification?

13. Which of the following steps you will be using in the discovery phase?

14. Select the correct statement which applies to logistic regression

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

16. Suppose there are three events then which formula must always be equal to P (E1|E2, E3)?

17. In unsupervised learning which statements correctly applies

18. Select the correct statement which applies to Principal component analysis (PCA)

19. Projecting a multi-dimensional dataset onto which vector has the greatest variance?

20. Which technique you would be using to solve the below problem statement? "What is the probability that individual customer will not repay the loan amount?"

21. Select the correct statement which applies to logistic regression

22. Reducing the data from many features to a small number so that we can properly visualize it in

two or three dimensions. It is done in_______

23. You are working on a Data Science project and during the project you have been gibe a responsibility to interview all the stakeholders in the project. In which phase of the project you are?

24. In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because

25. Which of the following is a Continuous Probability Distributions?

26. Your customer provided you with 2. 000 unlabeled records three groups.

What is the correct analytical method to use?

27. 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?

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

29. Select the correct statement which applies to Supervised learning

30. 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

31. What are the advantages of the mutual information over the Pearson correlation for text classification problems?

32. Refer to image below

33. What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

34. Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because

35. 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).

36. The figure below shows a plot of the data of a data matrix M that is 1000 x 2.





Which line represents the first principal component?

37. In which of the following scenario we can use naTve Bayes theorem for classification

38. Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367.

However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.

39. Which of the following problem you can solve using binomial distribution

40. Support vector machines (SVMs) are a set of supervised learning methods used for

41. What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

42. You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related.

What is the best solution for that?