Question 1

What is the primary advantage of using AI for credit scoring over traditional methods?

Options :

A : AI eliminates the need for regulatory compliance in credit decisions.

B : AI models are less complex and easier to implement.

C : AI can incorporate a wider range of data sources and features.

D : AI requires less data to make predictions.

Answer: C

Question 2

What is the significance of ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores in evaluating text summarization models in a business context?

Options :

A : ROUGE scores assess the originality of the generated summaries

B : ROUGE scores evaluate the similarity between the generated summary and a reference summary

C : ROUGE scores measure the computational efficiency of summarization models

D : ROUGE scores determine the length of the summary relative to the original text

Answer: B

Question 3

What is a significant challenge of AutoML (Automated Machine Learning) in complex enterprise AI workflows?

Options :

A : It exclusively generates linear regression

B : It limits the amount of training data used

C : It primarily works with shallow networks

D : It reduces model transparency and control

Answer: D

Question 4

What is the primary goal of supervised learning in artificial intelligence?

Options :

A : To map input data to known output labels

B : To discover hidden patterns in data

C : To generate new data points similar to a given dataset

D : To allow the AI model to learn without any labeled data

Answer: A

Question 5

Which metric is most critical for evaluating the success of a machine learning model designed to predict customer churn?

Options :

A : Mean squared error for accuracy

B : ROC-AUC for model generalization

C : Cross-entropy loss for classification

D : F1 score to balance precision and recall

Answer: D