IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working with IBM Watsonx to develop a generative AI solution that automatically generates product descriptions for an e-commerce website. The descriptions need to be concise, factual, and include important product features like size, color, and material.
Which prompt design approach would best ensure the output meets these requirements?

A) "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material."
B) "Generate a product description that highlights the unique aspects of the product and uses emotional language to engage the reader."
C) "Write a summary that provides information on each product, making the content engaging, humorous, and memorable."
D) "Generate a creative and imaginative product description for the items listed below."

2. Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A) To generate new responses based on training data, without relying on external data sources.
B) To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.
C) To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
D) To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.

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) Use static prompt text and avoid variables to maintain a consistent structure for each report.
C) Adjust the temperature setting to generate more creative and detailed reports.
D) Increase the randomness parameter to encourage diverse sections in the report structure.

4. In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages.
Which of the following best describes when to use a vector database as part of the Retrieval-Augmented Generation (RAG) approach?

A) When the dataset consists mainly of structured tabular data and relational queries.
B) When the data consists of diverse unstructured documents, and you need to retrieve semantically similar content using dense vector representations.
C) When there is a need to perform efficient keyword-based search on highly structured documents.
D) When there is a requirement to process large volumes of streaming data in real-time, and exact matching is the priority.

5. You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)

A) Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
B) Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.
C) Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
D) Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.
E) Normalize the vector embeddings after generation to ensure they are comparable during retrieval.

Solutions: