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

A company is deploying a Large Language Model (LLM) to automate customer support across multiple channels (e.g., chat, email, and social media). During the deployment, they notice that the model's performance varies significantly across channels. What could be the most likely cause of this issue?

Options :

A : The LLM was not trained on multi-channel data

B : Insufficient hardware resources allocated to the model

C : The LLM is not configured to handle multiple languages

D : Differences in data distribution across channels

Answer: D

Question 3

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

Question 4

You are working with a Large Language Model (LLM) trained on general-purpose data, but now you need it to perform well on legal document processing. After fine-tuning the model on a legal dataset, it still struggles with accurately interpreting certain legal terminologies and produces inconsistent outputs. What would be the most effective next step to improve the model's performance?

Options :

A : Apply data augmentation techniques on the general-purpose dataset.

B : Fine-tune the model on a larger legal dataset.

C : Integrate domain-specific embeddings for legal terms.

D : Add more general-purpose data to the training set.

Answer: C

Question 5

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

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