Online Access Free Databricks-Certified-Professional-Data-Scientist Practice Test

You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.

• A.Percentage of the customer should be given loan or not
• B.Probability of the customer default on loan repayment
• C.Percentage of the customer loan repayment capability
• D.The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".

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?

• A.Logistic Regression
• B.Item-based collaborative filtering
• C.Content-based filtering
• D.Naive Bayes classifier

Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e". The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?

• A.Option D
• B.Option B
• C.Option A
• D.Option C

Refer to the Exhibit.

In the Exhibit, the table shows the values for the input Boolean attributes "A", "B", and "C". It also shows the values for the output attribute "class". Which decision tree is valid for the data?

• A.Tree B
• B.Tree C
• C.Tree A
• D.Tree D

Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

• A.Support Vector Count
• B.Mean Absolute Error
• C.Cluster Density
• D.Sum of Absolute Errors