PASS4SURE IBM C1000-185 EXAM PREP, RELIABLE C1000-185 TEST NOTES

Pass4sure IBM C1000-185 Exam Prep, Reliable C1000-185 Test Notes

Pass4sure IBM C1000-185 Exam Prep, Reliable C1000-185 Test Notes

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Tags: Pass4sure C1000-185 Exam Prep, Reliable C1000-185 Test Notes, Exam C1000-185 Collection, Exam C1000-185 PDF, Latest C1000-185 Exam Answers

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q177-Q182):

NEW QUESTION # 177
You are working on a project that requires generating a large volume of product descriptions for an e-commerce website. The descriptions must be unique, creative, and optimized for SEO. The client has specified that the descriptions must also include certain technical product specifications, but they should not be overly mechanical or robotic in tone. Your team has access to an LLM pre-trained on large-scale, general-purpose corpora.
Based on this scenario, what would be your first step to design the most effective Generative AI solution for this task?

  • A. Fine-tune the pre-trained LLM on a domain-specific dataset of e-commerce product descriptions with an emphasis on SEO-friendly language.
  • B. Use the pre-trained LLM directly without any modification and feed it the product specifications, prompting it to generate descriptions.
  • C. Use prompt engineering to add technical specifications dynamically to generated text without fine-tuning the model.
  • D. Optimize the generation process using greedy decoding to ensure concise and accurate descriptions.

Answer: A


NEW QUESTION # 178
When deploying a machine learning model in a highly regulated industry (e.g., healthcare or finance), which strategy is most effective to ensure ongoing model performance while adhering to AI governance standards?

  • A. Perform real-time continuous training of the model using live data from the production environment
  • B. Implement a model performance monitoring framework with fairness and bias detection metrics
  • C. Deploy the model with hard-coded rules to ensure it does not drift from expected behavior
  • D. Ensure model interpretability is maximized by simplifying the architecture to a linear model

Answer: B


NEW QUESTION # 179
When analyzing the results of a prompt tuning experiment, which two of the following actions are most appropriate if you observe a consistently high variance in model predictions across different prompt templates? (Select two)

  • A. Tune the prompt templates further by standardizing the structure
  • B. Increase the number of training samples used for tuning
  • C. Increase the batch size during training
  • D. Enable regularization techniques like dropout
  • E. Add more layers to the model to increase complexity

Answer: A,B


NEW QUESTION # 180
You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

  • A. Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.
  • B. Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
  • C. Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.
  • D. Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.

Answer: A


NEW QUESTION # 181
How can developers leverage Prompt Lab in IBM Watsonx to build reusable prompts for a conversational AI system that handles multiple topics?

  • A. By utilizing Prompt Lab's automatic suggestion feature, which builds new prompts based on the system's training data.
  • B. By generating a unique prompt for each possible user query to ensure maximum precision.
  • C. By creating a single prompt template that adapts to all user inputs regardless of the subject.
  • D. By designing prompts with dynamic variables for user-specific information, allowing for flexibility across different topics.

Answer: D


NEW QUESTION # 182
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