DEMO QUESTIONS

Q1:

You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)

A: K-means

B: K-nearest neighbors

C: Lasso regression

D: Logistic regression

E: Ridge regression

Correct Answer: C, E

Q2:

Which of the following tests should be performed at the production level before deploying a newly retrained model?

A: A/Btest

B: Performance test

C: Security test

D: Unit test

Correct Answer: B

Q3:

You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)

A: K-means

B: K-nearest neighbors

C: Lasso regression

D: Logistic regression

E: Ridge regression

Correct Answer: C, E

Q4:

You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?

A: When it is necessary to save computational time.

B: When the categories of the dependent variable are not linearly separable.

C: When the distribution of the dependent variable is Gaussian.

D: When there is high correlation among the features.

Correct Answer: B

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