Databricks Databricks-Certified-Professional-Data-Scientist Exam With Confidence Using Practice Dumps

Exam Code:

Databricks-Certified-Professional-Data-Scientist

Exam Name:

Databricks Certified Professional Data Scientist Exam

Certification:

Databricks Certification

Vendor:

Databricks

Questions:

138

Last Updated:

Apr 22, 2026

Exam Status:

Stable

Databricks-Certified-Professional-Data-Scientist: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Professional Data Scientist Exam Questions and Answers

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Question 1

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...

Options:

L2 is the sum of the square of the weights, while L1 is just the sum of the weights

L1 is the sum of the square of the weights, while L2 is just the sum of the weights

L1 gives Non-sparse output while L2 gives sparse outputs

None of the above

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Question 2

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?

Options:

Naive Bayes

Logistic Regression

Random Decision Forests

Any one of the above

Question 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