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[Hardware] IBM C1000-185 Exam Topic, C1000-185 Valid Exam Blueprint

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【Hardware】 IBM C1000-185 Exam Topic, C1000-185 Valid Exam Blueprint

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q219-Q224):NEW QUESTION # 219
What is a key advantage of using prompt variables in IBM Watsonx for a chatbot application that needs to handle multiple user intents?
  • A. Prompt variables enable developers to predefine multiple static responses for each possible user input.
  • B. Using prompt variables ensures that the model will always select the most relevant response based on past conversations.
  • C. Prompt variables allow for the dynamic injection of user-specific details into the response, improving personalization.
  • D. Prompt variables allow the model to automatically detect the user's intent, ensuring accurate responses.
Answer: C

NEW QUESTION # 220
You are testing a new version of a prompt template designed to improve the accuracy of responses from a generative model deployed on IBM Watsonx. After deploying the new prompt version, you need to ensure that it performs better or at least as well as the previous version.
Which of the following approaches provides the most reliable method for testing the performance of the new prompt template version?
  • A. Test the new prompt in production without monitoring and observe user feedback to gauge performance.
  • B. Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.
  • C. Run a series of A/B tests comparing the new prompt template to the old one, using a set of predetermined metrics, such as response accuracy and completion time.
  • D. Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.
Answer: C

NEW QUESTION # 221
You have been using a pre-trained foundation model for a financial text summarization application. While the model is generating summaries that are generally accurate, it sometimes fails to handle domain-specific financial jargon. You are considering whether it's time to tune the model to optimize its performance for this task.
Which of the following conditions would most strongly justify tuning the foundation model for your specific use case?
  • A. The model produces a high number of tokens in each output, which increases the cost of usage.
  • B. The model generates output that is highly relevant to general topics but often misinterprets industry-specific terms like "leverage" or "derivative."
  • C. The model's accuracy fluctuates based on the length of the input text.
  • D. The model exhibits acceptable performance but occasionally generates off-topic responses unrelated to financial data.
Answer: B

NEW QUESTION # 222
While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?
  • A. Breaking complex prompts into simpler, sequential prompts.
  • B. Using stop tokens early in the prompt to minimize generation length.
  • C. Including multiple tasks in a single prompt to maximize efficiency.
  • D. Switching from a narrative-style prompt to a bulleted list format.
Answer: B

NEW QUESTION # 223
Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?
  • A. Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.
  • B. Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.
  • C. Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.
  • D. Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
Answer: A

NEW QUESTION # 224
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