Question 1

Which of the following are examples of supervised learning? (Select two)

Options :

A : Neural network

B : Document classification

C : Association rule learning

D : Clustering

E : Linear regression

Answer: A,E

Question 2

A financial company uses a generative AI model to assign credit limits to new customers. Thecompany wants to make the decision-making process of the model more transparent to itscustomers.

Options :

A : Use a rule-based system instead of an ML model.

B : Apply explainable AI techniques to show customers which factors influenced the model's decision.

C : Develop an interactive UI for customers and provide clear technical explanations about thesystem.

D : Increase the accuracy of the model to reduce the need for transparency.

Answer: B

Question 3

A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines. The company has a labeled dataset that contains user messages and output JSON files. Which solution will train the LLM for workflow automation?

Options :

A : Unsupervised learning

B : Continued pre-training

C : Fine-tuning

D : Reinforcement learning from human feedback (RLHF)

Answer: C

Question 4

A video streaming company is developing machine learning models to recommend content and analyze user interactions. The data science team needs to understand the specific capabilities of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).Which of the following would you suggest to the team?

Options :

A : Both RNNs and CNNs are used for video analysis

B : While RNNs are used for single image analysis, CNNs are used for video analysis

C : Both RNNs and CNNs are used for single image analysis

D : While CNNs are used for single image analysis, RNNs are used for video analysis

Answer: D

Question 5

What does an F1 score measure in the context of foundation model (FM) performance?

Options :

A : Model precision and recall.

B : Model speed in generating responses.

C : Financial cost of operating the model.

D : Energy efficiency of the model-s computations.

Answer: A

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