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[General] New C1000-185 Test Discount | C1000-185 Examcollection Questions Answers

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【General】 New C1000-185 Test Discount | C1000-185 Examcollection Questions Answers

Posted at yesterday 20:19      View:19 | Replies:0        Print      Only Author   [Copy Link] 1#
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q289-Q294):NEW QUESTION # 289
Prompt Lab in IBM Watsonx Generative AI offers several advantages for AI prompt engineering.
Which of the following best describes a primary benefit of using the Prompt Lab feature?
  • A. It provides a collaborative environment where multiple users can co-author prompts in real time.
  • B. It allows users to design custom AI models from scratch to handle specific tasks.
  • C. It guarantees that all generated responses adhere to industry-specific regulatory standards.
  • D. It enables users to test different versions of prompts and receive immediate feedback on their effectiveness.
Answer: D

NEW QUESTION # 290
A client is deploying a watsonx Generative AI solution to analyze customer feedback in real time. They require a cost-effective solution that can handle occasional traffic spikes but do not expect constant heavy loads.
What would be the most appropriate approach to minimize costs while ensuring adequate performance during traffic spikes?
  • A. Deploy the model using a serverless architecture with auto-scaling to handle sudden spikes in traffic.
  • B. Use a fixed number of on-premise servers that can handle peak traffic, but run them at a lower capacity during low-demand periods.
  • C. Deploy a single powerful GPU instance, relying on its ability to handle both regular and peak traffic without scaling.
  • D. Deploy a cloud-based instance with manual scaling to adjust resources when traffic spikes occur.
Answer: A

NEW QUESTION # 291
You are working on generating synthetic training data using IBM InstructLab to supplement a small dataset for a question-answering system.
Which strategy would most effectively enhance the dataset without introducing biases or artifacts?
  • A. Automatically generate synthetic data using a different model architecture than the one being fine-tuned.
  • B. Manually tweak each generated response to ensure it's free of errors and aligns with the intended task.
  • C. Generate a large amount of synthetic data by directly feeding the model with random prompts, ensuring data diversity.
  • D. Use prompts that closely mimic the structure and semantics of the real dataset's questions to maintain consistency.
Answer: D

NEW QUESTION # 292
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. Apply transfer learning with a dataset containing casual language
  • B. Use prompt engineering to guide the model towards empathetic responses
  • C. Adjust the model's max sequence length to encourage longer responses
  • D. Change the decoder strategy from greedy decoding to beam search to increase response quality
Answer: B

NEW QUESTION # 293
You are deploying an AI model to a production environment using watsonx.ai. The model is a fine-tuned GPT-based generative model designed to support real-time customer interactions. The deployment must ensure scalability, security, and maintain high availability.
Which deployment strategy should you choose to best meet these requirements?
  • A. Use a containerized deployment with Kubernetes on a multi-node GPU cluster to support auto-scaling and fault tolerance.
  • B. Deploy the model on an on-premises server with an isolated environment to ensure full control and security.
  • C. Deploy the model on a single cloud-based GPU instance, and enable scheduled maintenance to minimize downtime.
  • D. Deploy the model on multiple virtual machines, manually balancing traffic between instances.
Answer: A

NEW QUESTION # 294
......
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