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[General] Exam C1000-185 Forum & C1000-185 Testking Learning Materials

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【General】 Exam C1000-185 Forum & C1000-185 Testking Learning Materials

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q226-Q231):NEW QUESTION # 226
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. Integrate prompt versioning into the model deployment pipeline using automated versioning tools like Git.
  • B. Use endpoint monitoring tools only, without any versioning approach, to track and assess prompt changes in production.
  • C. Maintain different versions of the model and prompts by duplicating them across multiple endpoints without a centralized repository.
  • D. Use manual version control through logging and local storage.
Answer: A

NEW QUESTION # 227
You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?
  • A. Analyze token distribution patterns in the generated outputs
  • B. Use a higher learning rate to improve model convergence
  • C. Increase the number of training epochs to further refine the model
  • D. Apply model quantization to reduce the complexity of the output
Answer: A

NEW QUESTION # 228
Which of the following best describes the difference between a hard prompt and a soft prompt in the context of generative AI optimization?
  • A. A hard prompt consists of static, predefined instructions, while a soft prompt is optimized using continuous input embeddings that adapt based on the training context.
  • B. A hard prompt is used for deterministic output, while a soft prompt allows for random sampling.
  • C. A hard prompt encodes the model's hyperparameters, while a soft prompt adjusts the learning rate dynamically.
  • D. A hard prompt directly modifies the model architecture, while a soft prompt changes the model's training data.
Answer: A

NEW QUESTION # 229
After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)
  • A. Large difference between training and validation accuracy
  • B. High training accuracy
  • C. Low training loss
  • D. Low validation loss
  • E. Small difference between training and validation loss
Answer: D,E

NEW QUESTION # 230
You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?
  • A. Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
  • B. Replace all null values with zeros, as this will standardize the dataset.
  • C. Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.
  • D. Delete all rows containing null values in the 'ticket_description' field.
Answer: C

NEW QUESTION # 231
......
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