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[General] Latest C1000-185 Exam Cram | Exam C1000-185 Study Solutions

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【General】 Latest C1000-185 Exam Cram | Exam C1000-185 Study Solutions

Posted at 1/22/2026 16:05:12      View：54 | Replies：0        Print      Only Author   [Copy Link] 1#
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q252-Q257):NEW QUESTION # 252
Your team has developed an AI model that generates automated legal documents based on user inputs. The client, a large law firm, wants to deploy this model but has stringent security, compliance, and auditability requirements due to the sensitive nature of the data.
What is the most appropriate deployment strategy to meet these specific requirements?
  • A. Deploy the model on a public cloud with built-in encryption and use APIs to connect to the client's private data.
  • B. Use a private cloud with role-based access controls (RBAC) and ensure model activity is logged for auditing purposes.
  • C. Deploy the model using a serverless architecture to minimize operational overhead and maintain compliance.
  • D. Deploy the model on a hybrid cloud, with inference done on the client's on-premise servers and training done in the public cloud.
Answer: B

NEW QUESTION # 253
You are tasked with deploying a versioned prompt for a customer-facing generative AI application. The prompts are iteratively improved based on feedback, and you need to ensure that each version of the prompt is tracked and accessible for rollback in case a newer version produces worse results.
Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?
  • A. Leverage a cloud-based deployment pipeline with integrated versioning that automates prompt rollback and audit tracking.
  • B. Store each version of the prompt as a separate file in a local folder and manually deploy the desired version when needed.
  • C. Deploy prompts directly to production without versioning and manually track changes in a spreadsheet.
  • D. Use a version control system like Git to track prompt changes and synchronize the latest version with the deployment environment.
Answer: D

NEW QUESTION # 254
In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?
  • A. Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.
  • B. Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.
  • C. Quantization maintains model accuracy but doubles the computation required for inference.
  • D. Quantization always improves model performance but significantly increases model size.
Answer: B

NEW QUESTION # 255
When optimizing the tuning process in IBM watsonx Tuning Studio, what is the main purpose of fine-tuning metering options?
  • A. To enforce stricter thresholds for model accuracy in order to ensure high precision.
  • B. To track the computational cost and time for fine-tuning models based on resource consumption.
  • C. To limit the number of iterations allowed for model training.
  • D. To monitor the effectiveness of early stopping techniques applied during training.
Answer: B

NEW QUESTION # 256
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. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
  • B. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
  • C. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
  • D. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
Answer: A

NEW QUESTION # 257
......
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