Question 1

A team responsible for maintaining a generative AI application wants to ensure they are consistently using the most advanced language models available. Which strategy would be the most effective for staying up-to-date with the latest developments in AI models?

Options :

A : Subscribe to AI newsletters and blogs focused on practical AI applications

B : Periodically run internal benchmarks against the current model to check for performance degradation

C : Rely on the updates provided by the model vendors or cloud service providers

D : Regularly follow research papers published on platforms like arXiv and attend relevant AI conferences

Answer: D

Question 2

A retail company plans to deploy a generative AI model to automatically generate product descriptions for its e-commerce platform. What is the most important consideration to ensure that the generated descriptions are both engaging and factually accurate?

Options :

A : Ensuring the model can generate descriptions in multiple languages.

B : Deploying the model on a cloud platform to handle large-scale generation.

C : Training the model on a dataset that includes accurate product specifications and customer reviews.

D : Using the latest version of the AI model to ensure state-of-the-art language generation.

Answer: C

Question 3

You are building a recommendation system for a large-scale e-commerce platform that needs to analyze customer behavior, purchase history, and browsing patterns in real-time to provide personalized recommendations. The data is coming from multiple sources, some of which are too large to fit into memory. Which approach would be most effective for handling this complex scenario?

Options :

A : Storing all incoming data in a relational database and querying it in real-time

B : Using a rule-based engine to analyze and recommend products based on predefined criteria

C : Using a streaming data processing framework like Apache Kafka combined with in-memory processing

D : Loading all data into memory and running a batch process to generate recommendations

Answer: C

Question 4

You have trained two Large Language Models (LLMs) for sentiment analysis on customer reviews. Model A has a Cross-Entropy Loss of 0.25 and an F1-score of 0.82 on the validation set. Model B has a Cross-Entropy Loss of 0.30 but an F1-score of 0.87. Which model would you choose for deployment, and why?

Options :

A : Model B, because it has a higher loss, indicating it has learned more generalized features

B : Neither, because the difference in Cross-Entropy Loss is more significant than the difference in F1-score

C : Model B, because a higher F1-score indicates better balance between precision and recall

D : Model A, because lower Cross-Entropy Loss indicates better model confidence

Answer: C

Question 5

You are comparing two generative AI models for image captioning. Model A has a BLEU score of 0.75 and a loss of 0.35, while Model B has a BLEU score of 0.68 and a loss of 0.28. Which model should you deploy, and why?

Options :

A : Neither, because the BLEU score is not as important as the loss function in this scenario

B : Model A, because a higher BLEU score indicates better accuracy in generating captions

C : Model A, because it has both a higher BLEU score and lower loss

D : Model B, because a lower loss indicates the model is learning more effectively

Answer: B