RELIABLE BEST C1000-185 PREPARATION MATERIALS OFFER YOU ACCURATE TEST SCORE REPORT | IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE

Reliable Best C1000-185 Preparation Materials offer you accurate Test Score Report | IBM watsonx Generative AI Engineer - Associate

Reliable Best C1000-185 Preparation Materials offer you accurate Test Score Report | IBM watsonx Generative AI Engineer - Associate

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Tags: Best C1000-185 Preparation Materials, C1000-185 Test Score Report, C1000-185 Reliable Dumps Files, Latest C1000-185 Test Question, C1000-185 Exam Pass Guide

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q270-Q275):

NEW QUESTION # 270
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. A/B testing deployment
  • B. Canary deployment
  • C. Multi-model serving with dynamic routing
  • D. Blue-Green deployment

Answer: C


NEW QUESTION # 271
In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

  • A. Introduce explicit instructions in the prompt to avoid generating personal information
  • B. Increase the model's randomness by adjusting the temperature parameter
  • C. Reduce the model's token limit to restrict the amount of text generated
  • D. Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])

Answer: A


NEW QUESTION # 272
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

  • A. The model can generate overly creative or non-factual responses, leading to brand reputation damage.
  • B. The model might produce content that doesn't align with the cultural preferences of the user.
  • C. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
  • D. The model can generate outputs that are too general, failing to meet the specific needs of the user.

Answer: C


NEW QUESTION # 273
A machine learning engineer is optimizing a generative AI model for creative writing. They are debating the use of soft prompts over hard prompts.
What is the primary advantage of using soft prompts in this context, despite their complexity?

  • A. Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.
  • B. Soft prompts provide more direct control over the model's behavior by offering explicit, human-readable instructions.
  • C. Soft prompts allow for more nuanced control of the model's behavior through learned embeddings, which can adapt to a variety of tasks without requiring explicit human intervention.
  • D. Soft prompts enforce stricter generation outputs due to the deterministic nature of the learned embeddings, leading to higher consistency in results.

Answer: C


NEW QUESTION # 274
You are using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

  • A. Increase the number of fine-tuning epochs to ensure the model converges
  • B. Use prompt engineering to provide more explicit instructions for the model
  • C. Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data
  • D. Adjust the token length limit in the model's configuration

Answer: B


NEW QUESTION # 275
......

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