최신CertNexus Certified Artificial Intelligence Practitioner (CAIP) - AIP-210무료샘플문제

A. The null hypothesis is true, but is rejected.

B. The null hypothesis is false, but fails to be rejected.

C. The null hypothesis is false and is rejected.

D. The null hypothesis is true and fails to be rejected.

A. K-fold

B. Leave-one-out

C. Stratification

D. Bootstrapping

A. Overfitting

B. Slow convergence time

C. Higher compute resources

D. Low test accuracy

A. They are usually binary classifiers.

B. They are usually easier to explain to business stakeholders.

C. They are usually very good at solving non-linear problems.

D. They usually compromise on model accuracy for the sake of interpretability.

E. They are usually referred to as "black box" models.

A. By subtracting from the mean and dividing by the standard deviation.

B. So that they are on a similar scale.

C. Into the normal distribution.

D. To different scales from each other.

A. Model evaluation is defined as an external component.

B. Model evaluation and validation are the same.

C. Model validation is defined as a set of tasks to confirm the model performs as expected.

D. Model validation occurs before model evaluation.

A. If it was with consent of the person it is collected from.

B. If it is for a good cause.

C. If it was requested by the authority of law.

D. If it was collected accidentally.

E. If the data is only collected once.

A. The sample size is much larger than the number of features.

B. There is high collinearity among some of the features associated with the dependent variable.

C. There are many features with no association with the dependent variable.

D. The number of features is much larger than the sample size.

A. Increase the complexity of the model

B. Train the model for more epochs

C. Get more training data

D. Add features to training data