Question 1

Which privacy-preserving technique is most suitable for training machine learning models on sensitive data without exposing the data itself?

Options :

A : Data anonymization

B : Secure multi-party computation

C : Homomorphic encryption

D : Federated learning

Answer: D

Question 2

In a business context, what is a key advantage of using abstractive summarization over extractive summarization for summarizing lengthy reports?

Options :

A : Abstractive summarization is faster and easier to implement

B : Abstractive summarization always maintains the original wording of the text

C : Abstractive summarization reduces the need for data preprocessing

D : Abstractive summarization can generate more coherent and concise summaries

Answer: D

Question 3

How will quantum computing potentially revolutionize AI workflows in the near future?

Options :

A : By enabling AI to use less computational power

B : By reducing the need for machine learning entirely

C : By simplifying deep learning models for deployment

D : By solving complex optimization problems at speeds that classical computers cannot match

Answer: D

Question 4

Which of the following best explains the role of "Personalization" in enhancing chatbot interactions?

Options :

A : To optimize the response time consistently.

B : To standardize the conversation experience globally.

C : To tailor responses based on user preferences.

D : To reduce the complexity of chatbot architecture.

Answer: C

Question 5

Which of the following is a key ethical concern in artificial intelligence?

Options :

A : The high computational cost of training models.

B : Maximizing the accuracy of AI models.

C : Developing AI systems faster than competitors.

D : Ensuring that AI systems are interpretable and transparent.

Answer: D