EXAM C1000-185 REFERENCE | LATEST TEST C1000-185 EXPERIENCE

Exam C1000-185 Reference | Latest Test C1000-185 Experience

Exam C1000-185 Reference | Latest Test C1000-185 Experience

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q93-Q98):

NEW QUESTION # 93
You are tasked with fine-tuning a pre-trained large language model (LLM) for a customer service chatbot using IBM's InstructLab. You need to customize the LLM to improve its ability to handle specific user instructions related to order management, such as tracking orders, processing returns, and issuing refunds.
Which of the following components in InstructLab is the most critical for guiding the LLM to respond appropriately to these specific instructions?

  • A. The feedback loop system for real-time user input validation.
  • B. The data loader module for transforming data into tokenized inputs.
  • C. The prompt engineering interface for designing task-specific instructions.
  • D. The pre-processing pipeline for normalizing and standardizing the dataset.

Answer: C


NEW QUESTION # 94
You are tasked with building a Retrieval-Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure.
How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)

  • A. Use an unsupervised learning approach to generate embeddings, as labeled data is not necessary for improving retrieval performance.
  • B. Integrate additional metadata (e.g., document date, author) into the embedding representation to improve retrieval.
  • C. Rely solely on word-level embeddings to capture the meaning of legal phrases and concepts.
  • D. Train a domain-specific embedding model using legal documents to better capture the nuances of legal terminology.
  • E. Apply dimensionality reduction techniques like PCA to compress embeddings and improve retrieval speed.

Answer: B,D


NEW QUESTION # 95
You are using IBM's Tuning Studio to optimize a generative AI model. The model performance on your validation set has plateaued, and you suspect that tuning certain parameters in the Tuning Studio will improve the outcome.
Which of the following actions would be most effective in improving the model's performance?

  • A. Use a larger validation set
  • B. Enable early stopping
  • C. Increase the number of epochs
  • D. Decrease the batch size

Answer: B


NEW QUESTION # 96
You are implementing techniques to ensure that an IBM Watsonx Generative AI model does not expose any personal or sensitive information (PII) in its outputs.
What is the most effective technique for excluding personal information during the inference stage of the generative AI process?

  • A. Implement real-time filtering of model outputs using regular expressions to detect and mask personal information.
  • B. Train the model on synthetic data that does not include any personal information.
  • C. Use a greedy decoding strategy to limit the creativity of the model and prevent unexpected outputs.
  • D. Use temperature tuning to control the diversity of outputs, reducing the likelihood of personal information being revealed.

Answer: A


NEW QUESTION # 97
A healthcare organization is deploying a generative AI model to assist doctors in generating patient reports based on medical notes and test results. The organization has strict compliance requirements regarding patient data privacy (e.g., HIPAA in the U.S.) and needs to ensure that the model outputs are governed under their AI policies. You are tasked with integrating AI governance policies into the deployment to meet both ethical and legal standards.
Which AI governance measure is most important to implement during the deployment phase of this AI solution, given the healthcare organization's need for compliance with patient privacy regulations?

  • A. Deploying an explainability module to allow doctors to see the reasons behind each AI-generated suggestion, ensuring ethical use of the model.
  • B. Requiring doctors to manually review every AI-generated report before it is finalized to meet compliance with medical standards.
  • C. Implementing a system to track and log all model-generated inferences, flagging any that suggest potential bias or non-compliance with privacy regulations.
  • D. Ensuring that the model's training data includes only anonymized patient records to avoid potential data breaches.

Answer: C


NEW QUESTION # 98
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