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[General] C1000-185 Top Exam Dumps - C1000-185 Exam Labs

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【General】 C1000-185 Top Exam Dumps - C1000-185 Exam Labs

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q177-Q182):NEW QUESTION # 177
You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?
  • A. Increase the temperature from 0.5 to 1.5
  • B. Set the temperature to 0.0
  • C. Decrease the temperature from 0.9 to 0.3
  • D. Decrease the temperature from 0.8 to 0.6
Answer: C

NEW QUESTION # 178
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom-trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs.
Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?
  • A. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
  • B. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.
  • C. Deploy all AI assets as a single monolithic model on a multi-GPU setup, allowing scalability across multiple customers without the need for further customization.
  • D. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
Answer: A

NEW QUESTION # 179
While customizing an LLM in InstructLab to generate more human-like responses for a customer service chatbot, you notice that the responses are too formal and lack empathy.
Which of the following techniques will best address this problem and help tailor the model to generate more empathetic responses?
  • A. Change the decoder strategy from greedy decoding to beam search to increase response quality
  • B. Use prompt engineering to guide the model towards empathetic responses
  • C. Apply transfer learning with a dataset containing casual language
  • D. Adjust the model's max sequence length to encourage longer responses
Answer: B

NEW QUESTION # 180
Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?
  • A. Prompt variables ensure that the same text input will always yield the same output, improving consistency.
  • B. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
  • C. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
  • D. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
Answer: D

NEW QUESTION # 181
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 provide more direct control over the model's behavior by offering explicit, human-readable instructions.
  • B. Soft prompts improve the simplicity of the model's overall structure, making it easier to debug and interpret the generation process.
  • 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 # 182
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