Try and practice the latest IBM : C1000-185 real questions & answers

You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

• A. Experiment with a smaller model to reduce complexity and increase inference speed.
• B. Add more domain-specific training data to improve the model's understanding of technical vocabulary.
• C. Increase the learning rate for better adaptation to specialized data.
• D. Perform prompt engineering by providing specific task instructions for summarization.
• E. Use greedy decoding during inference to generate concise summaries.

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 provide more direct control over the model's behavior by offering explicit, human-readable instructions.
• B. Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.
• 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 enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.

A generative AI team is optimizing a model for text summarization. The team uses both hard prompts and soft prompts during training.
What is the primary reason a soft prompt might provide less explainability compared to a hard prompt?

• A. Soft prompts are used exclusively in unsupervised learning settings, where explainability is inherently lower due to lack of labeled data.
• B. Soft prompts are explicitly written by humans and include domain-specific instructions, making them harder to explain.
• C. Soft prompts involve complex embedding vectors, which are learned and difficult for humans to interpret directly.
• D. Soft prompts include manually designed templates that change dynamically, reducing transparency.

You are working on a project that requires generating a large volume of product descriptions for an e-commerce website. The descriptions must be unique, creative, and optimized for SEO. The client has specified that the descriptions must also include certain technical product specifications, but they should not be overly mechanical or robotic in tone. Your team has access to an LLM pre-trained on large-scale, general-purpose corpora.
Based on this scenario, what would be your first step to design the most effective Generative AI solution for this task?

• A. Use the pre-trained LLM directly without any modification and feed it the product specifications, prompting it to generate descriptions.
• B. Use prompt engineering to add technical specifications dynamically to generated text without fine-tuning the model.
• C. Optimize the generation process using greedy decoding to ensure concise and accurate descriptions.
• D. Fine-tune the pre-trained LLM on a domain-specific dataset of e-commerce product descriptions with an emphasis on SEO-friendly language.

You are tasked with designing a prompt for an IBM Watsonx model that will automate customer support responses for a company that sells technical products. The use case requires the model to respond accurately to specific customer inquiries about product troubleshooting.
What is the most effective prompt to use for this scenario?

• A. "Write a creative explanation of how to fix our product when it fails to function properly."
• B. "Based on the following error description, provide a step-by-step solution: 'The device won't power on even after charging for 3 hours.' Be specific and concise in your response."
• C. "Help a customer resolve an issue with our product."
• D. "Write a generic response to help customers with any issue they may have."