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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q376-Q381):

NEW QUESTION # 376
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. "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material."
D. "Generate a creative and imaginative product description for the items listed below."

Answer: C

NEW QUESTION # 377
You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.
Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

A. Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
B. Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.
C. Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.
D. Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.

Answer: C

NEW QUESTION # 378
Which of the following best describes the process of large-scale iterative alignment tuning in the context of customizing LLMs with InstructLab?

A. Fine-tuning the model exclusively on binary classification tasks to improve its generalization on all other tasks
B. A single training run of the model on a dataset to generate better predictions for a fixed number of prompts
C. Direct training of the model on an expanded version of the dataset, without adjusting prompts or training tasks
D. Repeated fine-tuning of a model using reinforcement learning, focusing on aligning its outputs with human preferences across a diverse set of tasks

Answer: D

NEW QUESTION # 379
When optimizing the tuning process in IBM watsonx Tuning Studio, what is the main purpose of fine-tuning metering options?

A. To limit the number of iterations allowed for model training.
B. To enforce stricter thresholds for model accuracy in order to ensure high precision.
C. To monitor the effectiveness of early stopping techniques applied during training.
D. To track the computational cost and time for fine-tuning models based on resource consumption.

Answer: D

NEW QUESTION # 380
You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

A. Store all prompt versions directly in the model's code repository, updating the main branch with each new version.
B. Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
C. Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.
D. Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.

Answer: D

NEW QUESTION # 381
......

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