Real Databricks Certified Professional Data Scientist Exam Questions For Preparation

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

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

3. 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

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

5. 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

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

7. 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?"

8. 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

9. 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?

10. 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?

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

What is the correct analytical method to use?

12. 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?

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

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

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

16. 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?

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

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