Question 1

Which technique is most effective in reducing the computational cost of a CNN deployed in a business application without significantly compromising accuracy?

Options :

A : Increasing the depth of the CNN

B : Switching to a sigmoid activation function

C : Applying model pruning

D : Using a larger batch size during training

Answer: C

Question 2

What is the primary difference between supervised and unsupervised learning?

Options :

A : Supervised learning optimizes for accuracy, while unsupervised learning optimizes for precision.

B : Supervised learning is only applicable to regression problems, while unsupervised learning is used for classification.

C : Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

D : Supervised learning can only be used for small datasets, while unsupervised learning works with large datasets.

Answer: C

Question 3

What does the Schmarzo theorem identify as a key challenge when it comes to the valuation of intangible data assets?

Options :

A : Valuation uncertainty

B : Asset depreciation

C : Market unpredictability

D : Utilization difficulty

Answer: A

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

What is the concept of "AI maturity" in the context of value-driven AI strategies, and why is it important for CEOs to understand this concept?

Options :

A : AI maturity represents an organization’s ability to scale AI applications and fully integrate them into core business processes.

B : AI maturity is the point at which AI can replace human decision-making entirely within an organization.

C : AI maturity refers to the development of AI technologies in academic research and its readiness for business adoption.

D : AI maturity is the timeline for AI deployment and the speed of AI model development.

Answer: A

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