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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with designing a prompt template to assist a chatbot in generating professional email responses for customer service inquiries. The system should prioritize politeness, clarity, and conciseness.
What elements should be included in the prompt template to achieve the best results, considering optimal behavior of a large language model (LLM)? (Select two)

A) Include examples of informal customer service responses for variability
B) Specify the output tone as polite and professional
C) Ask the model to generate multiple versions of the response and rank them
D) Provide the customer's emotional context for better alignment with the tone
E) Instruct the model to limit responses to a specific character count

2. Prompt Lab in IBM Watsonx Generative AI offers several advantages for AI prompt engineering.
Which of the following best describes a primary benefit of using the Prompt Lab feature?

A) It guarantees that all generated responses adhere to industry-specific regulatory standards.
B) It provides a collaborative environment where multiple users can co-author prompts in real time.
C) It enables users to test different versions of prompts and receive immediate feedback on their effectiveness.
D) It allows users to design custom AI models from scratch to handle specific tasks.

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

4. You are fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A) Add more general-purpose data to the fine-tuning dataset
B) Increase the number of layers in the model
C) Use domain-specific tokenization during fine-tuning
D) Increase the model's batch size during training

5. A developer is using a GitHub Code Retrieval API to help build a search engine that can locate relevant code snippets from public repositories. The API is designed to retrieve code based on the semantic similarity of the query (e.g., a description of what the code does) to the code itself.
What is the primary advantage of using a vector-based approach for code retrieval in this scenario?

A) It provides real-time updates to the code embeddings, ensuring the latest code is always retrieved.
B) It retrieves code snippets based on the semantic similarity between the query and the code, enabling the discovery of relevant code even when the query and code do not use the same keywords.
C) It speeds up retrieval by limiting the search to repositories where the user has committed code in the past.
D) It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code.

Solutions: