Real Databricks Certified Professional Data Scientist Exam Questions For Preparation

Real Databricks Certified Professional Data Scientist Exam Questions For Preparation

Databricks Certified Professional Data Scientist is a great Databricks exam, which assesses the understanding of the basics of machine learning, the steps in the machine learning lifecycle, the understanding of basic machine learning algorithms and techniques, and the understanding of the basics of machine learning model management. Real Databricks Certified Professional Data Scientist Exam Questions are released at ITExamShop to ensure that you can prepare for the exam well, then finally, you can pass Databricks Certified Professional Data Scientist exam in the first attempt.

Databricks Certified Professional Data Scientist Free Questions Are Below For Checking:

Page 1 of 2

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?


 

Leave a Reply

Your email address will not be published.