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[General] C1000-185 Online Training | Original C1000-185 Questions

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【General】 C1000-185 Online Training | Original C1000-185 Questions

Posted at 3 day before      View：7 | Replies：2        Print      Only Author   [Copy Link] 1#
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q209-Q214):NEW QUESTION # 209
When using few-shot prompting, which of the following is a common limitation you may encounter while working with Watsonx AI for text generation tasks?
  • A. Few-shot prompting cannot handle open-ended tasks, as it only works for structured data inputs.
  • B. Few-shot prompting requires the model to be retrained with additional examples.
  • C. The model may overfit to the few examples provided, leading to overly specific responses.
  • D. Few-shot prompting is only effective for classification tasks and cannot be used for text generation.
Answer: C

NEW QUESTION # 210
You are developing a solution that requires generating various reports using IBM Watsonx. To maintain efficiency and avoid redundant prompt creation, you decide to implement reusable prompts.
Which of the following best summarizes the value of using reusable prompts in this context?
  • A. Reusable prompts eliminate the need for monitoring model performance since they consistently deliver accurate results.
  • B. Reusable prompts automatically adjust their structure depending on the input, eliminating the need for dynamic parameters or customization.
  • C. They allow for faster deployment of new AI-driven solutions since the same prompt templates can be adapted for multiple tasks with minimal changes.
  • D. Reusable prompts ensure that the AI model continues to evolve without additional training, as the prompts themselves become more refined over time.
Answer: C

NEW QUESTION # 211
You are implementing a Retrieval-Augmented Generation (RAG) system to enhance a large language model's (LLM) ability to answer questions based on an external document store.
What role do embeddings play in the RAG architecture, and how can you optimize them for more relevant document retrieval? (Select two)
  • A. Embeddings are only used during the generation phase of RAG to enhance language model outputs.
  • B. Embeddings are vector representations of text, used to measure the similarity between queries and documents.
  • C. Fine-tuning the embedding model with task-specific data can lead to more accurate retrievals.
  • D. The cosine similarity metric is typically less effective than Euclidean distance for comparing embeddings in RAG systems.
  • E. Increasing the dimensionality of embeddings always improves retrieval accuracy by providing a more detailed representation of the documents.
Answer: B,C

NEW QUESTION # 212
In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)
  • A. "based on their interest"
  • B. "Recommend products"
  • C. "for"
  • D. "[customer_name]"
  • E. "[product_category]"
Answer: D,E

NEW QUESTION # 213
When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?
  • A. Decreasing the value of the temperature parameter to 0.2
  • B. Using Top-k Sampling with a k value of 100
  • C. Reducing the beam size in beam search from 5 to 1
  • D. Increasing the temperature parameter to 1.5
Answer: A

NEW QUESTION # 214
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Posted at 3 day before        Only Author  2#
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