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最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題:

1. You are generating product descriptions for an online marketplace using a generative AI model. The output is coherent but tends to repeat the same phrases and words excessively. You decide to apply a repetition penalty to reduce this repetition while keeping the temperature set to a value that maintains creativity in the text generation.
Which of the following adjustments would best achieve this goal?

A) Set repetition penalty to 1.5 and maintain temperature at 0.8
B) Set repetition penalty to 0.0
C) Set repetition penalty to 1.0 and decrease temperature from 0.8 to 0.3
D) Set repetition penalty to 2.0 and increase temperature from 0.7 to 1.2

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

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

4. In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?

A) Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.
B) Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.
C) Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
D) Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.

5. You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)

A) Set a high penalty for repetition to encourage varied recommendations
B) Increase the temperature to 1.5 to maximize creativity in suggestions
C) Set the temperature to 0.1 for highly deterministic responses
D) Apply a low top-k value (e.g., k=10) to restrict randomness
E) Use a top-p (nucleus) sampling value of 0.95 for diverse, relevant outputs

問題與答案：