MARVELOUS C1000-185 OFFICIAL STUDY GUIDE TO OBTAIN IBM CERTIFICATION

Marvelous C1000-185 Official Study Guide to Obtain IBM Certification

Marvelous C1000-185 Official Study Guide to Obtain IBM Certification

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q158-Q163):

NEW QUESTION # 158
You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?

  • A. Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.
  • B. Embeddings store the entire content of documents, which is then directly passed to the generative model.
  • C. Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
  • D. Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.

Answer: D


NEW QUESTION # 159
You are deploying a generative AI model for a financial services company. The model is responsible for automating customer support and providing recommendations. Due to the sensitive nature of financial data, the company emphasizes the need for robust AI governance.
What governance mechanism should you prioritize to ensure compliance with data privacy regulations and maintain trust in AI outputs?

  • A. Ensuring model version control to track changes and updates made to the model during the deployment process.
  • B. Regularly retraining the model to avoid performance degradation due to data drift.
  • C. Implementing role-based access control (RBAC) to restrict who can interact with the model.
  • D. Using AI explainability techniques to make the model's decisions transparent to regulators and customers.

Answer: D


NEW QUESTION # 160
In IBM Watsonx's Prompt Lab, example input prompts can be used to improve the effectiveness of generated responses.
Which of the following best describes a key benefit of utilizing example input prompts in Prompt Lab?

  • A. Example input prompts help generate responses that are more aligned with the specific context or style intended by the user.
  • B. Example input prompts allow the model to learn new concepts and update its knowledge base dynamically.
  • C. Example input prompts automatically adjust the model's training dataset for more accurate predictions.
  • D. Example input prompts guarantee consistency across all outputs, regardless of variability in user-provided data.

Answer: A


NEW QUESTION # 161
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. Ensure model interpretability is maximized by simplifying the architecture to a linear model
  • C. Implement a model performance monitoring framework with fairness and bias detection metrics
  • D. Deploy the model with hard-coded rules to ensure it does not drift from expected behavior

Answer: C


NEW QUESTION # 162
IBM Watsonx Tuning Studio provides metering options to help monitor and optimize fine-tuning processes.
Which of the following best describes how these metering options can optimize resource usage during fine-tuning?

  • A. Fine-tuning metering enables hyperparameter search, automatically testing multiple configurations to find the most optimal one for the current task.
  • B. Fine-tuning metering provides insights into token usage and computational costs, allowing users to set budget constraints and minimize expenses during the tuning process.
  • C. Fine-tuning metering options automatically halt the training process if no significant improvements in model accuracy are observed, reducing unnecessary resource usage.
  • D. Fine-tuning metering adjusts the learning rate dynamically to optimize both training speed and accuracy.

Answer: B


NEW QUESTION # 163
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