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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

Question 2

What is the key difference between zero-shot and few-shot prompting when used in generative AI models like IBM Watsonx?

A. Zero-shot prompting provides feedback to the model during inference, while few-shot does not allow model feedback.
B. Few-shot prompting requires a model to have pre-trained examples of the task, while zero-shot does not.
C. In zero-shot prompting, the model is fine-tuned before answering, but in few-shot prompting, no fine-tuning occurs.
D. Zero-shot prompting does not provide any examples in the prompt, while few-shot prompting includes multiple task examples.

Question 3

You are building a generative AI conversational model to act as a virtual assistant that helps users schedule meetings. The goal is to create a prompt that initiates a conversation in a way that guides the user to provide relevant scheduling information.
Which prompt would be the most effective for this use case?

A. "Ask the user when they want to schedule their meeting."
B. "Guide the user through scheduling a meeting by asking about their preferred date, time, and attendees."
C. "Provide a list of all upcoming events in the user's calendar and ask if they want to add another."
D. "Please provide the user's calendar availability."

Question 4

You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

A. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
B. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
C. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
D. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.

Question 5

A machine learning engineer is optimizing a generative AI model for creative writing. They are debating the use of soft prompts over hard prompts.
What is the primary advantage of using soft prompts in this context, despite their complexity?

A. Soft prompts enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.
B. Soft prompts provide more direct control over the model's behavior by offering explicit, human-readable instructions.
C. Soft prompts allow for more nuanced control of the model's behavior through learned embeddings, which can adapt to a variety of tasks without requiring explicit human intervention.
D. Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.

Solutions: