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Practical IBM C1000-185: Clear IBM watsonx Generative AI Engineer - Associate Exam - Top PassCollection C1000-185 Prep Guide

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q285-Q290):

NEW QUESTION # 285
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. Using AI explainability techniques to make the model's decisions transparent to regulators and customers.
B. Implementing role-based access control (RBAC) to restrict who can interact with the model.
C. Ensuring model version control to track changes and updates made to the model during the deployment process.
D. Regularly retraining the model to avoid performance degradation due to data drift.

Answer: A

NEW QUESTION # 286
You are fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A. Increase the number of layers in the model
B. Increase the model's batch size during training
C. Add more general-purpose data to the fine-tuning dataset
D. Use domain-specific tokenization during fine-tuning

Answer: D

NEW QUESTION # 287
In a Retrieval-Augmented Generation (RAG) setup, you notice that the model is generating responses that are not always relevant to the query, despite the knowledge base containing useful information.
What could be the most likely cause of this issue, and how should you address it?

A. The retrieval mechanism might be failing to fetch the most relevant documents from the knowledge base, so you should improve the search algorithm or use a better ranking system.
B. The problem likely lies with the input format, so changing all queries to a pre-structured format (like templates) will ensure the retrieval and generation stages perform optimally.
C. The knowledge base might contain outdated or irrelevant documents, so removing all non-recent documents would ensure the model generates more relevant responses.
D. The model is over-relying on the retrieval system and ignoring the language model's ability to generate coherent responses, so you should disable the retrieval component for general questions.

Answer: A

NEW QUESTION # 288
Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?

A. DevOps Engineer
B. Data Scientist
C. Data Engineer
D. Solution Architect

Answer: D

NEW QUESTION # 289
You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?

A. Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
B. Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
C. Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.
D. Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.

Answer: B

NEW QUESTION # 290
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