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C1000-185 Latest Exam Answers | Exam Dumps C1000-185 Demo

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q114-Q119):

NEW QUESTION # 114
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. "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."
B. "Help a customer resolve an issue with our product."
C. "Write a creative explanation of how to fix our product when it fails to function properly."
D. "Write a generic response to help customers with any issue they may have."

Answer: A

NEW QUESTION # 115
After tuning a generative AI model to produce more concise legal document summaries, you notice that while the summaries are accurate, they tend to be overly verbose. The tuning report shows that the model's perplexity is relatively high, suggesting that it is struggling with token prediction uncertainty, possibly due to an overly complex output format.
Which of the following tuning parameters would you most likely adjust to address the verbosity issue without reducing accuracy?

A. Decrease the learning rate
B. Decrease the temperature
C. Increase the number of epochs
D. Increase the maximum token length

Answer: B

NEW QUESTION # 116
You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user's request.
How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?

A. Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.
B. Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.
C. Rely on LangChain's memory module to remember previous user queries and provide real-time data access.
D. Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user's request.

Answer: D

NEW QUESTION # 117
Which of the following best describes the difference between a hard prompt and a soft prompt in the context of generative AI optimization?

A. A hard prompt directly modifies the model architecture, while a soft prompt changes the model's training data.
B. A hard prompt is used for deterministic output, while a soft prompt allows for random sampling.
C. A hard prompt consists of static, predefined instructions, while a soft prompt is optimized using continuous input embeddings that adapt based on the training context.
D. A hard prompt encodes the model's hyperparameters, while a soft prompt adjusts the learning rate dynamically.

Answer: C

NEW QUESTION # 118
After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A. High training accuracy
B. Low training loss
C. Low validation loss
D. Small difference between training and validation loss
E. Large difference between training and validation accuracy

Answer: C,D

NEW QUESTION # 119
......

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