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IBM C1000-185 試験シラバストピック:

IBM watsonx Generative AI Engineer - Associate 認定 C1000-185 試験問題：

1. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
B) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
C) When the model needs to generate a strictly factual output with minimal deviation from the prompt.
D) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.

2. You are implementing techniques to ensure that an IBM Watsonx Generative AI model does not expose any personal or sensitive information (PII) in its outputs.
What is the most effective technique for excluding personal information during the inference stage of the generative AI process?

A) Use temperature tuning to control the diversity of outputs, reducing the likelihood of personal information being revealed.
B) Use a greedy decoding strategy to limit the creativity of the model and prevent unexpected outputs.
C) Implement real-time filtering of model outputs using regular expressions to detect and mask personal information.
D) Train the model on synthetic data that does not include any personal information.

3. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
B) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
C) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
D) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.
E) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.

4. You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A) Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
B) Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
C) Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6
D) Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3

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 first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
B) 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.
C) The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
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.

質問と回答：