IBM C1000-185 QUIZ & C1000-185 STUDY GUIDE & C1000-185 TRAINING MATERIALS

IBM C1000-185 Quiz & C1000-185 study guide & C1000-185 training materials

IBM C1000-185 Quiz & C1000-185 study guide & C1000-185 training materials

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Tags: C1000-185 Guaranteed Questions Answers, C1000-185 Exam Quiz, C1000-185 Latest Dumps Ebook, C1000-185 Exam Vce Free, C1000-185 Examcollection Dumps Torrent

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q179-Q184):

NEW QUESTION # 179
You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

  • A. Perform prompt engineering by providing specific task instructions for summarization.
  • B. Increase the learning rate for better adaptation to specialized data.
  • C. Experiment with a smaller model to reduce complexity and increase inference speed.
  • D. Add more domain-specific training data to improve the model's understanding of technical vocabulary.
  • E. Use greedy decoding during inference to generate concise summaries.

Answer: A,D


NEW QUESTION # 180
A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

  • A. Using human-in-the-loop (HITL) methods for real-time validation
  • B. Increasing the model's response diversity by adjusting top-p sampling
  • C. Deploying rule-based post-processing filters to validate the output
  • D. Switching to greedy decoding for more deterministic responses

Answer: C


NEW QUESTION # 181
You are working as a generative AI engineer and have developed a custom large language model (LLM) optimized for a specific use case. You are tasked with deploying this model on the IBM Watsonx platform.
Which of the following steps is most essential to ensure the successful deployment of your custom model, given that the model uses a third-party transformer architecture?

  • A. Set up auto-scaling in the IBM Watsonx environment to handle large numbers of simultaneous model inference requests.
  • B. Ensure that the model's training data is in a proprietary IBM format, as only Watsonx-specific formats are supported for custom model deployments.
  • C. Containerize the model using Docker or an equivalent containerization tool, ensuring that all required dependencies, such as transformers, tokenizers, and necessary packages, are included.
  • D. Modify the model to use IBM's proprietary transformer architecture, as third-party architectures are not supported by Watsonx.

Answer: C


NEW QUESTION # 182
You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.
Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

  • A. Apply top-k sampling with k = 50 to ensure diverse and creative outputs.
  • B. Implement early stopping criteria based on token repetition to avoid lengthy generation.
  • C. Reduce the batch size to decrease training time and speed up generation.
  • D. Use beam search with a beam width of 1 to minimize computation time.
  • E. Increase the model's learning rate to accelerate content generation.

Answer: A,B


NEW QUESTION # 183
You are tasked with deploying two generative AI systems: one for document summarization and another for question-and-answer (Q&A). Both systems need to handle large volumes of unstructured text, but they differ in their response time requirements and interaction with users.
Which deployment strategy would be most appropriate for this scenario?

  • A. Use a single deployment strategy where both the summarization and Q&A systems are integrated into a shared model that processes all requests through the same inference pipeline.
  • B. Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers.
  • C. Use a rule-based model for summarization and a machine learning-based model for Q&A, combining them into a single service endpoint for deployment.
  • D. Deploy the summarization model as a batch processing system, while the Q&A system is deployed as an on-demand service using a low-latency inference model.

Answer: D


NEW QUESTION # 184
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