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最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題:

1. You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

A) Choose a diverse set of prompts that span different domains unrelated to the original dataset.
B) Customize prompt templates to closely mimic the structure and format of the original training data.
C) Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
D) Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.

2. You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A) Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
B) Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
C) Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
D) Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.
E) Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.

3. You are tasked with explaining the outcomes produced by a Watsonx Generative AI model based on specific prompts.
Which of the following approaches is most effective in ensuring transparency and understanding of how the model arrives at its decisions?

A) Explaining the optimization process that minimized the model's loss function
B) Providing an interpretable

4. IBM Watsonx's Prompt Lab offers various options to refine prompts for generating more effective AI outputs.
Which of the following is an accurate description of an editing option available in Prompt Lab?

A) Users can use Prompt Lab to train the AI model on new datasets and retrain it based on prompt performance.
B) Prompt Lab allows users to experiment with prompt structures, such as adjusting token limits or adding contextual instructions, to improve responses.
C) Users can disable the model's access to certain pre-trained knowledge domains within Prompt Lab to focus its output on specific areas.
D) Users can apply real-time machine learning to modify the underlying model parameters within Prompt Lab.

5. You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?

A) The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
B) The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
C) The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.
D) The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.

問題與答案：