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[General] Highlighted Features of IBM C1000-185 Exam Practice Questions

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【General】 Highlighted Features of IBM C1000-185 Exam Practice Questions

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P.S. Free 2026 IBM C1000-185 dumps are available on Google Drive shared by VCEPrep: https://drive.google.com/open?id=1wG02DgZ2E_EiPe3AHgo6fzQ4xQAhh2q1
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q317-Q322):NEW QUESTION # 317
When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?
  • A. Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.
  • B. Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.
  • C. Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.
  • D. Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.
Answer: A

NEW QUESTION # 318
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 exhibits acceptable performance but occasionally generates off-topic responses unrelated to financial data.
  • B. The model produces a high number of tokens in each output, which increases the cost of usage.
  • C. The model generates output that is highly relevant to general topics but often misinterprets industry-specific terms like "leverage" or "derivative."
  • D. The model's accuracy fluctuates based on the length of the input text.
Answer: C

NEW QUESTION # 319
Your team has developed multiple versions of a custom Watsonx Generative AI model to address different use cases. During deployment, the client requires that all model versions be deployed concurrently, allowing requests to be routed to the appropriate model based on the input data.
What deployment strategy would best accommodate this requirement?
  • A. Blue-Green deployment
  • B. A/B testing deployment
  • C. Multi-model serving with dynamic routing
  • D. Canary deployment
Answer: C

NEW QUESTION # 320
You are tasked with designing a Retrieval-Augmented Generation (RAG) system using embeddings to improve the response quality of a generative AI model.
In this context, what are embeddings used for, and how do they contribute to enhancing the generative AI's performance?
  • A. Embeddings serve as a form of knowledge storage within the generative model, allowing it to answer questions without retrieving external information.
  • B. Embeddings provide a summary of the input data, which the model then uses to generate its final output without retrieving external content.
  • C. Embeddings compress the input data to reduce computational load, improving the efficiency of the retrieval and generation process.
  • D. Embeddings transform the input data into high-dimensional vectors, capturing semantic similarities between the input query and potential retrieval candidates to provide contextually relevant information for the generative model.
Answer: D

NEW QUESTION # 321
You are tasked with configuring a generative model to assist legal professionals by generating contract clauses. The model should produce complete, contextually relevant clauses but should avoid overly long and redundant text. You must ensure that the generation process stops appropriately once a clause is completed.
Which of the following strategies is the most appropriate for configuring stopping criteria?
  • A. Set a maximum token limit of 500 and implement a fixed length stopping criterion.
  • B. Set the top-k value to 1 and use the default maximum token limit, relying on the model's internal stopping criteria.
  • C. Use a stop sequence based on legal clause delimiters and impose a minimum token limit of 50.
  • D. Use a beam search algorithm and impose a strict length restriction of 100 tokens.
Answer: C

NEW QUESTION # 322
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