Question 1

You are assisting in developing a software module for a generative AI application. The senior engineer asks you to write documentation for the codebase. What is the best practice for creating this documentation to ensure it meets professional standards?

Options :

A : Only document the code that you have written yourself

B : Use auto-generated documentation tools exclusively

C : Focus on documenting the key functions, classes, and algorithms with clear explanations

D : Document every line of code in detail

Answer: C

Question 2

You are building a generative AI model for a medical chatbot that will provide diagnostic suggestions based on patient inputs. The dataset includes a large amount of unstructured text data from various medical records. The accuracy of the model is critical, but so is the model's ability to handle highly domain-specific terminology and nuances. Which of the following strategies would be most effective in ensuring the model understands and accurately responds to the medical context?

Options :

A : Preprocess the dataset to remove all complex medical terms to simplify the data

B : Use a generic pre-trained model and fine-tune on the medical dataset without additional preprocessing

C : Label all instances of medical jargon as generic terms in the dataset

D : Leverage a pre-trained medical LLM (like BioBERT) and fine-tune it on your specific dataset

Answer: D

Question 3

You are developing a chatbot using a generative AI model to assist users with technical troubleshooting. The responses generated by the model are often too verbose, overwhelming the user with unnecessary details. What prompt engineering strategy would you use to make the responses more concise and user-friendly?

Options :

A : Use multiple prompts to break down the response into smaller sections

B : Explicitly instruct the model to generate concise and to-the-point responses in the prompt

C : Set the model to a higher temperature to reduce verbosity

D : Lengthen the prompt to include more background information

Answer: B

Question 4

You have been tasked with fine-tuning a generative AI model for creating personalized content recommendations on a streaming platform. During testing, the model tends to recommend similar content repeatedly, which reduces user engagement. What approach should you take to enhance the diversity of recommendations while maintaining relevance?

Options :

A : Lower the model’s temperature during inference

B : Apply a rule-based filter post-processing to exclude similar content

C : Increase the size of the training dataset without altering the model

D : Incorporate a diversity penalty during model training

Answer: D

Question 5

You are tasked with improving an AI model designed to assist medical professionals by providing diagnostic suggestions based on patient data. However, the model has been found to give inaccurate suggestions in certain rare cases, which could lead to incorrect treatments. The senior team member suggests conducting an experiment to evaluate the model's performance on rare cases and determine if incorporating human feedback can enhance its accuracy. Which experimental approach would be most effective in improving the model’s accuracy on rare cases?

Options :

A : Increase the overall size of the training dataset by including more common cases.

B : Conduct targeted fine-tuning of the model using a dataset enriched with rare cases and assess performance.

C : Train the model with reinforcement learning using feedback from common cases.

D : Use unsupervised learning techniques to identify patterns in rare cases without human intervention.

Answer: B

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