Question 1

A financial institution is analyzing a dataset to classify customers as high or low risk. The dataset includes some labeled data but primarily consists of unlabeled data points. They want a model that can be applied to new customer data in the future. Which semi-supervised learning approach would be most suitable?

Options :

A : Transductive learning with label propagation

B : Inductive learning with self-training

C : Transductive learning with clustering

D : Ensemble learning

Answer: B

Question 2

A Board of Directors at a global tech firm is considering developing a data-collection AI that could enhance user experience but involves intrusive tracking. According to rule consequentialism, how should they decide?

Options :

A : Approve the AI if it increases company revenue and shareholder value

B : Approve the AI if it adheres to rules that generally maximize trust and benefit users

C : Reject the AI if it does not comply with all regional data privacy laws

D : Reject the AI as it could potentially reduce the number of future data projects

Answer: B

Question 3

A team develops a credit scoring model using only borrower income as a feature and notices that it fails to account for variations in credit score. Which of the following could resolve this issue?

Options :

A : Adding interaction terms and other relevant variables

B : Increasing regularization

C : Using a linear relationship assumption

D : Reducing the size of the training dataset

Answer: A

Question 4

Which term best describes a dataset that includes a mix of organized, structured financial data and unstructured text data from customer reviews?

Options :

A : Structured data

B : Unstructured data

C : Semi-structured data

D : Binary data

Answer: C

Question 5

A generative AI provider in China is designing its service to align with Chinese regulations. What is a key obligation it must meet under the Generative AI Measures?

Options :

A : Ensure all content generated is free from any factual errors.

B : Prioritize content generation for government-endorsed topics only.

C : Prevent the generation of illegal and discriminatory content, respecting privacy rights.

D : Avoid transparency in algorithm usage to maintain competitive advantage.

Answer: C

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