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[General] Pass Guaranteed Quiz IBM - C1000-185–Efficient Study Plan

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【General】 Pass Guaranteed Quiz IBM - C1000-185–Efficient Study Plan

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q127-Q132):NEW QUESTION # 127
You are working on deploying a generative AI model into production. The goal is to ensure that different versions of prompts can be tracked and rolled back in case of degradation in the model's performance.
Which of the following strategies would best address versioning for deployment?
  • A. Maintain different versions of the model and prompts by duplicating them across multiple endpoints without a centralized repository.
  • B. Use manual version control through logging and local storage.
  • C. Integrate prompt versioning into the model deployment pipeline using automated versioning tools like Git.
  • D. Use endpoint monitoring tools only, without any versioning approach, to track and assess prompt changes in production.
Answer: C

NEW QUESTION # 128
You are selecting a model to fine-tune using Tuning Studio for a financial application that requires high accuracy and domain-specific language understanding.
Which type of model should you select to maximize fine-tuning efficiency and performance?
  • A. A large pre-trained language model that has been fine-tuned on generic business communication.
  • B. A model pre-trained on financial and business data but with limited language capabilities.
  • C. A small pre-trained model specifically designed for open-domain tasks.
  • D. A pre-trained model that has been optimized for creative text generation.
Answer: B

NEW QUESTION # 129
During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)
  • A. Introduce dropout regularization during fine-tuning to prevent overfitting.
  • B. Use early stopping based on the validation set performance.
  • C. Increase the size of the model to better capture complex patterns in the training data.
  • D. Remove all regularization and fine-tune the model on the training set until convergence.
  • E. Fine-tune the model for more epochs to ensure the model has fully learned from the training data.
Answer: A,B

NEW QUESTION # 130
In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?
  • A. Retraining the model based on emerging trends in data
  • B. Deployment to production with regular monitoring and logging
  • C. Initial testing on synthetic datasets and model validation
  • D. Iterative prompt tuning based on A/B test results and feedback loops
Answer: D

NEW QUESTION # 131
Which of the following techniques can be most effectively used to mitigate the generation of hate speech, abuse, and profanity in generative AI models when applying prompt engineering?
  • A. Restricting the model's ability to generate certain words or phrases using stop-word lists
  • B. Using Greedy Decoding to ensure that the model outputs the most likely sequence of tokens
  • C. Fine-tuning the model with specific datasets curated to exclude offensive content
  • D. Applying Token Regularization to limit the diversity of generated responses
Answer: C

NEW QUESTION # 132
......
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