IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE TRAINING TORRENT & C1000-185 UPDATED DUMPS & IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE LATEST MATERIAL

IBM watsonx Generative AI Engineer - Associate training torrent & C1000-185 updated dumps & IBM watsonx Generative AI Engineer - Associate latest material

IBM watsonx Generative AI Engineer - Associate training torrent & C1000-185 updated dumps & IBM watsonx Generative AI Engineer - Associate latest material

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q129-Q134):

NEW QUESTION # 129
You are designing a customer support chatbot using watsonx.ai as the primary generative model. You want to enhance the chatbot's capabilities by integrating it with IBM Watson Assistant to handle structured conversations while allowing watsonx.ai to generate responses for open-ended queries.
Which integration approach would most effectively combine both services while maintaining optimal performance and accuracy?

  • A. Use Watson Discovery to preprocess all queries before routing them to either Watson Assistant or watsonx.ai based on query complexity.
  • B. Create separate chat interfaces for Watson Assistant and watsonx.ai, and allow the user to choose which system to query based on their needs.
  • C. Train Watson Assistant to handle both structured and unstructured conversations, while using watsonx.ai only for rare edge cases that Watson Assistant cannot manage.
  • D. Implement Watson Assistant for structured conversations and use a middleware layer that dynamically routes complex, open-ended queries to watsonx.ai, returning the results within the same session.

Answer: D


NEW QUESTION # 130
You are working on a project where the AI model needs to generate personalized customer support responses based on various input fields like customer name, issue type, and product details. To make the system scalable and flexible, you decide to use prompt variables in your implementation.
Which of the following statements accurately describe the benefits of using prompt variables in this scenario? (Select two)

  • A. Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.
  • B. Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on-the-fly customization of responses.
  • C. Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.
  • D. Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.
  • E. Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.

Answer: A,D


NEW QUESTION # 131
You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

  • A. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
  • B. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
  • C. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
  • D. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.

Answer: C


NEW QUESTION # 132
You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision.
What does this likely indicate about the model's performance, and how should you approach further tuning?

  • A. The model is overly conservative, missing relevant details; focus on improving recall.
  • B. The model is generating too many irrelevant details; focus on improving precision.
  • C. The model's length of generated summaries is too short, indicating underfitting.
  • D. The model's summaries are incomplete, indicating poor understanding of the source material; consider fine-tuning the pre-trained embeddings.

Answer: B


NEW QUESTION # 133
You have successfully deployed a custom model using IBM Watson Machine Learning. Now, an application needs to access the model for inference.
Which of the following configurations will ensure that the application has appropriate access to the deployed model?

  • A. Assign the "viewer" role to the application's service account in Watson Machine Learning
  • B. Implement OAuth2 authentication using IBM Cloud IAM for securing the API.
  • C. Use the default IAM policies for all users in the IBM Cloud project
  • D. Create a public-facing API endpoint without authentication

Answer: B


NEW QUESTION # 134
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