Real Databricks Certified Professional Data Scientist Dumps With Actual Questions

1. You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

2. What type of output generated in case of linear regression?

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

4. In which of the scenario you can use the regression to predict the values

5. 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?

6. Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

7. Select the statement which applies correctly to the Naive Bayes

8. 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?

9. Refer to Exhibit

In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan.

Which analytical method could produce the probabilities needed to build this exhibit?

10. 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?

11. A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

12. 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.

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

14. As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user.

So Which recommender system technique benefits most from additional user preference data?

15. Which of the following technique can be used to the design of recommender systems?

16. 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

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

18. In unsupervised learning which statements correctly applies

19. 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)?

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

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

What is the correct analytical method to use?

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

23. 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?

24. The method based on principal component analysis (PCA) evaluates the features according to

25. Which is an example of supervised learning?

26. You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user.

What kind of this recommendation engine is?

27. RMSE measures error of a predicted

28. 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?

29. 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?"

30. Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array.

So what is the primary reason of the hashing trick for building classifiers?

31. You have modeled the datasets with 5 independent variables called A, B, C, D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).

Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer

32. Clustering is a type of unsupervised learning with the following goals

33. Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

34. You have data of 10.000 people who make the purchasing from a specific grocery store. You also have their income detail in the data. You have created 5 clusters using this data. But in one of the cluster you see that only 30 people are falling as below 30, 2400, 2600, 2700, 2270 etc."

What would you do in this case?

35. In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

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

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

38. Which of the following true with regards to the K-Means clustering algorithm?

39. A problem statement is given as below

Hospital records show that of patients suffering from a certain disease, 75% die of it.

What is the probability that of 6 randomly selected patients, 4 will recover?

Which of the following model will you use to solve it?

40. 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

41. Which of the following statement true with regards to Linear Regression Model?

42. Select the correct statement which applies to Supervised learning

43. A data scientist wants to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level.

What is the most appropriate method for this project?

44. Question-34. Stories appear in the front page of Digg as they are "voted up" (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members.

Which of the following technique is used to make such recommendation engine?

45. What describes a true limitation of Logistic Regression method?

46. Select the choice where Regression algorithms are not best fit

47. Refer to image below

48. 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

49. Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow. Green).

Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

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

So which statement correctly applies?