20 More Demo Questions in C1000-185 Free Dumps (Part 3, Q81-Q100) Are Available: Help You Check the C1000-185 Dumps (V8.02) Today

1. You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.

Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

2. Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.

What steps should you take to mitigate both the risks of data bias and poisoning?

3. You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.

What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?

4. You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.

Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

5. You are tasked with fine-tuning a large language model (LLM) using IBM's InstructLab to improve performance for a specific customer service task. The goal is to enhance the model’s ability to answer questions related to account management and customer complaints.

Which of the following actions is NOT a component of the fine-tuning process in InstructLab?

6. Prompt Lab in IBM Watsonx Generative AI offers several advantages for AI prompt engineering.

Which of the following best describes a primary benefit of using the Prompt Lab feature?

7. 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?

8. A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.

Which of the following techniques would most effectively reduce this risk in a production environment?

9. You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.

Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

10. You are tasked with optimizing a generative AI model’s output for a natural language generation task.

Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

11. You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.

Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

12. You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.

What is the most important function of LlamaIndex in managing document retrieval?

13. In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

14. Which of the following techniques can be most effectively used to mitigate the generation of hate speech, abuse, and profanity in generative AI models when applying prompt engineering?

15. In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

16. You are tasked with deploying a generative AI solution for a client who operates in the healthcare sector. Due to the sensitive nature of the data, the client requires a highly secure deployment with continuous monitoring for regulatory compliance.

Which role is primarily responsible for ensuring the AI solution is compliant with these security and regulatory requirements?

17. As a Generative AI engineer, you're tasked with optimizing the performance and cost-efficiency of a model by adjusting the model parameters.

Given that your objective is to reduce the cost of generation while maintaining acceptable quality, which of the following parameter changes is most likely to result in cost savings?

18. You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.

Which of the following is the best indicator that it is time to tune the foundation model for your task?

19. When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

20. You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.

Under which of the following circumstances is the use of a vector database most appropriate?