Question 1

Which approach is most effective for minimizing bias in AI systems during the data preprocessing and model training phases?

Options :

A : Ensure the dataset includes equal representation from all demographic groups.

B : Apply adversarial training to explicitly reduce bias during model training.

C : Focus on optimizing model accuracy above all other metrics.

D : Manually remove any data that seems biased from the dataset.

Answer: A

Question 2

When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?

Options :

A : Chi-squared test

B : Paired t-test

C : Mann-Whitney U test

D : ANOVA test

Answer: B

Question 3

You are working on a project that involves generating product descriptions for a large e-commerce platform. The platform sells a wide range of products, from electronics to clothing. The generative AI model currently in use produces high-quality descriptions for electronics but struggles with fashion items, often generating vague or inaccurate content. What is the most effective strategy to improve the model's performance across all product categories?

Options :

A : Switch to a smaller, less complex model to handle fashion descriptions separately

B : Use a rule-based system to generate fashion descriptions instead of AI

C : Reduce the complexity of the model to prevent overfitting

D : Fine-tune the model on a dataset specifically focused on fashion items

Answer: D

Question 4

What is one of the primary challenges associated with the deployment of generative AI models, and how can it also present an opportunity?

Options :

A : Model interpretability is difficult, but it can also lead to advancements in explainable AI.

B : Bias in the training data can lead to unfair or harmful outputs, but it also presents an opportunity to develop more ethical AI.

C : Data privacy concerns limit the availability of training data, but this also drives the development of synthetic data generation.

D : High computational requirements can increase costs, but it also provides an opportunity to invest in more energy-efficient hardware.

Answer: B

Question 5

You have deployed a Large Language Model (LLM) for a customer support chatbot. The chatbot performs well in most scenarios but struggles with ambiguous queries, often giving incomplete or irrelevant responses. Which of the following approaches is most likely to improve the chatbot's performance in handling ambiguous queries?

Options :

A : Implement a fallback mechanism to ask clarifying questions when the query is ambiguous.

B : Reduce the number of layers in the LLM to simplify its output.

C : Increase the temperature setting in the model’s inference process.

D : Increase the size of the training dataset with more diverse examples.

Answer: A

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