最も有効な C1000-185 試験参考書

You are developing a natural language generation (NLG) system for financial report summaries. The system needs to generate concise, high-quality summaries for different financial instruments. Tuning Studio is available as a tool to improve the performance of the model.
Which of the following capabilities of Tuning Studio is most helpful for improving the NLG system's performance in this domain-specific application?

• A. It provides real-time feedback on model performance during text generation.
• B. It helps in selecting the most relevant datasets from a pre-existing library for fine-tuning the model.
• C. It enables the fine-tuning of pre-trained models on custom datasets to improve accuracy and relevance to the specific domain.
• D. It allows you to visually inspect and analyze the model's token-by-token generation process.

In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

• A. Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.
• B. Hard prompts are less efficient because they need to be re-trained with each task, while soft prompts are more versatile and adaptive across multiple tasks.
• C. A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.
• D. A soft prompt is a fixed string of text used in fine-tuning, while a hard prompt adjusts dynamically based on input data.

You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

• A. Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.
• B. Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
• C. Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.
• D. Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.

Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?

• A. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
• B. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
• C. Prompt variables ensure that the same text input will always yield the same output, improving consistency.
• D. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.

Which two of the following are key benefits of using IBM Watsonx's Tuning Studio for fine-tuning generative AI models on specific tasks? (Select two)

• A. Significantly reduces the risk of overfitting without manual intervention
• B. Automatically generates prompt templates based on data input
• C. Automatically selects the best model architecture for the task
• D. Provides automated hyperparameter search and tuning strategies
• E. Offers real-time validation of tuning experiments to optimize model performance