Question 1

What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

Options :

A : To customize the model for a specific task by feeding it task-specific content

B : To feed the model a large volume of data from a wide variety of subjects

C : To use the model in a production, research, or test environment

D : To randomize all the statistical weights of the neural networks

Answer: C

Question 2

Why should artificial intelligence developers always take inputs from diverse sources?

Options :

A : To investigate the model requirements properly

B : To perform exploratory data analysis

C : To determine where and how the dataset is produced

D : To cover all possible cases that the model should handle

Answer: D

Question 3

What is one of the objectives of Al in the context of digital transformation?

Options :

A : To become essential to the success of the digital economy

B : To reduce the need for Internet connectivity

C : To replace all human tasks with automation

D : To eliminate the need for data privacy

Answer: A

Question 4

What is a principle thatguides organizations, government, and developers towards the ethical use of Al?

Options :

A : Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models

B : The value of Al models must only be measured in financial gain.

C : Al models must ensure data privacy and confidentiality.

D : Al models must always agree with the user-s point of view.

Answer: C

Question 5

What is the significance ofparameters in Large Language Models (LLMs)?

Options :

A : Parameters are used to parse image, audio, and video data in LLMs.

B : Parameters are used to decrease the size of the LLMs.

C : Parameters are used to increase the size of the LLMs.

D : Parameters are statistical weights inside of the neural network of LLMs.

Answer: D