Question 1

A marketing team is using a Large Language Model (LLM) to generate product descriptions for an e-commerce website. However, they find that the descriptions generated are often too generic and do not highlight the unique features of each product. Which prompt engineering strategy would be most effective in generating more detailed and specific product descriptions?

Options :

A : Including specific product features and target audience details within the prompt.

B : Using a shorter prompt to allow the LLM more freedom in generating creative content.

C : Increasing the temperature parameter during generation to add more variation in the output.

D : Asking the LLM to generate multiple descriptions and then manually selecting the best one.

Answer: A

Question 2

You are tasked with developing a generative AI model for a client that needs to generate creative marketing content based on customer behavior data. The data includes customer purchase history, browsing patterns, and social media interactions. Which of the following approaches would be most effective in ensuring that the generated content is both relevant and creative? (Select two)

Options :

A : Use a rule-based system to generate content based on predefined templates.

B : Use transfer learning to apply insights from a different but similar domain to the customer behavior data.

C : Fine-tune a pre-trained Large Language Model (LLM) on the specific customer behavior dataset.

D : Employ a reinforcement learning framework to dynamically adjust the content based on real-time customer feedback.

E : Leverage unsupervised learning to identify clusters in customer data and use these clusters to guide content generation.

Answer: C,D

Question 3

When working with several large datasets (some of which are larger-than-memory) for machine learning exercises, which strategy is most effective for ingesting and preparing the data?

Options :

A : Employing data chunking and streaming techniques to load and process the datasets in parts

B : Loading all datasets simultaneously into a high-performance in-memory database

C : Using a cloud-based data warehouse to store and query the data directly

D : Compressing the datasets to fit them into memory before processing

Answer: A

Question 4

You are deploying a generative AI model on an edge device with limited computational resources. The model must generate responses in near real-time while ensuring data privacy. Which strategy would be the most effective for balancing performance, accuracy, and privacy?

Options :

A : Perform all computations on the cloud

B : Use federated learning without model optimization

C : Use model quantization and secure enclave processing

D : Deploy the full model with no optimization

Answer: C

Question 5

You are working with a large dataset containing millions of transactions. Your goal is to identify patterns of fraudulent activity. Which data mining technique would be most effective for this purpose?

Options :

A : Association Rule Mining

B : Principal Component Analysis (PCA)

C : K-Means Clustering

D : Linear Regression

Answer: A

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