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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A) Perform a spelling correction on the entire dataset to remove any language inconsistencies.
B) Manually categorize each interaction and organize them into a taxonomy tree structure.
C) Convert all text to lowercase to ensure uniformity in the dataset.
D) Tokenize the dataset before curating it and mapping it to the taxonomy tree.

2. You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A) Focus on improving the ROUGE-L score while increasing memory utilization
B) Reduce memory utilization and maintain BLEU and ROUGE-L scores
C) Increase memory utilization to reduce BLEU and ROUGE-L scores
D) Maximize BLEU score and reduce memory utilization

3. You are refining a prompt for an AI model that generates financial reports using IBM watsonx Prompt Lab.
To ensure the AI produces structured outputs such as sections on "Revenue," "Expenses," and "Net Profit," which of the following is the most effective prompt editing option to achieve this goal?

A) Increase the randomness parameter to encourage diverse sections in the report structure.
B) Adjust the temperature setting to generate more creative and detailed reports.
C) Incorporate specific section headers as part of the prompt to guide the model in generating the desired report format.
D) Use static prompt text and avoid variables to maintain a consistent structure for each report.

4. You are tasked with securing an endpoint for a generative AI model that interacts with external applications.
Which of the following practices is MOST effective in ensuring both security and stability of the model's endpoint in a production environment?

A) Focus exclusively on endpoint authentication without enforcing any rate limits to prevent user frustration.
B) Implement strict IP whitelisting and authentication, but allow unfiltered prompts for flexibility.
C) Limit the number of API calls per minute, restrict prompt lengths, and apply input validation to prevent malicious queries.
D) Allow all external applications unrestricted access to the endpoint to facilitate wider model usage.

5. A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A) Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.
B) Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.
C) Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.
D) Apply model quantization to optimize the LLM for handling long legal documents more efficiently.

Solutions: