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[Hardware] Your Ultimate Resource Actual of IBM C1000-185 Questions

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【Hardware】 Your Ultimate Resource Actual of IBM C1000-185 Questions

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q67-Q72):NEW QUESTION # 67
You are building a customer support chatbot using IBM watsonx.ai and Watson Assistant. The chatbot must use watsonx.ai's large language model (LLM) to generate dynamic responses and Watson Assistant to manage dialog and interaction flow.
What is the most efficient way to integrate these two services to deliver an optimal solution?
  • A. Use Watson Assistant as the primary interface and call watsonx.ai's LLM through an API for generating dynamic responses in specific intents.
  • B. Use Watson Assistant to directly generate all responses, bypassing watsonx.ai's LLM.
  • C. Deploy watsonx.ai's LLM within Watson Assistant by embedding the LLM directly into the Watson Assistant environment.
  • D. Build a separate microservice for each service, allowing Watson Assistant and watsonx.ai's LLM to operate independently, with no communication between them.
Answer: A

NEW QUESTION # 68
In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?
  • A. Deployment to production with regular monitoring and logging
  • B. Retraining the model based on emerging trends in data
  • C. Initial testing on synthetic datasets and model validation
  • D. Iterative prompt tuning based on A/B test results and feedback loops
Answer: D

NEW QUESTION # 69
When setting up a tuning experiment in IBM watsonx's Tuning Studio, which of the following best describes the process for optimizing a model's hyperparameters?
  • A. Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.
  • B. All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.
  • C. Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.
  • D. Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.
Answer: A

NEW QUESTION # 70
You are designing a prompt to converse with a model for multilingual translation.
How would you frame the prompt to translate an English business email to Japanese, ensuring that the translated email is formal and appropriate for a business setting in Japan?
  • A. "Translate this business email into Japanese but do not consider the tone or formalities."
  • B. "Translate this business email into Japanese using informal language."
  • C. "Translate the following business email into Japanese, focusing on word-for-word accuracy."
  • D. "Translate this business email into Japanese, making sure the tone is formal and culturally appropriate for a business context."
Answer: D

NEW QUESTION # 71
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 an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.
  • B. The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
  • C. 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.
  • D. The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
Answer: C

NEW QUESTION # 72
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